Did you know that the concept of deep learning goes way back to the 1950s? However, it is only in recent years that this technology has created a tremendous amount of buzz (and for good reason!). A subset of machine learning, deep learning is inspired by the structure of the human brain, making it fascinating to learn about.

In this episode, Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Hemant Gahankari about deep learning concepts, including how Convolution Neural Networks work, and help you get your deep learning basics right.

Oracle MyLearn: https://mylearn.oracle.com/

Oracle University Learning Community: https://education.oracle.com/ou-community

LinkedIn: https://www.linkedin.com/showcase/oracle-university/

X (formerly Twitter): https://twitter.com/Oracle_Edu

Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.

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Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26

Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor.

Nikita: Hi everyone! Last week, we covered the new MySQL HeatWave Implementation Associate certification. So do go check out that episode if it interests you.

Lois: That was a really interesting discussion for sure. Today, we’re going to focus on the basics of deep learning with our Senior Principal OCI Instructor, Hemant Gahankari.

00:58

Nikita: Hi Hemant! Thanks for being with us today. So, to get started, what is deep learning?

Hemant: Deep learning is a subset of machine learning that focuses on training Artificial Neural Networks to solve a task at hand. Say, for example, image classification. A very important quality of the ANN is that it can process raw data like pixels of an image and extract patterns from it. These patterns are treated as features to predict the outcomes. 

Let us say we have a set of handwritten images of digits 0 to 9. As we know, everyone writes the digits in a slightly different way. So how do we train a machine to identify a handwritten digit? For this, we use ANN. 

ANN accepts image pixels as inputs, extracts patterns like edges and curves and so on, and correlates these patterns to predict an outcome. That is what digit does the image has in this case. 

02:04

Lois: Ok, so what you’re saying is given a bunch of pixels, ANN is able to process pixel data, learn an internal representation of the data, and predict outcomes. That’s so cool! So, why do we need deep learning?

Hemant: We need to specify features while we train machine learning algorithm. With deep learning, features are automatically extracted from the data. Internal representation of features and their combinations is built to predict outcomes by deep learning algorithms. This may not be feasible manually.  Deep learning algorithms can make use of parallel computations. For this, usually data is split into small batches and process parallelly. So these algorithms can process large amount of data in a short time to learn the features and their combinations. This leads to scalability and performance. In short, deep learning complements machine learning algorithms for complex data for which features cannot be described easily. 

03:13

Nikita: What can you tell us about the origins of deep learning?

Hemant: Some of the deep learning concepts like artificial neuron, perceptron, and multilayer perceptron existed as early as 1950s. One of the most important concept of using backpropagation for training ANN came in 1980s. 

In 1990s, convolutional neural network were also introduced for image analysis task. Starting 2000, GPUs were introduced. And 2010 onwards, GPUs became cheaper and widely available. This fueled the widespread adoption of deep learning uses like computer vision, natural language processing, speech recognition, text translation, and so on. 

In 2012, major networks like AlexNet and Deep-Q Network were built. 2016 onward, generative use cases of the deep learning also started to come up. Today, we have widely adopted deep learning for a variety of use cases, including large language models and many other types of generative models. 

04:29

Lois: Hemant, what are various applications of deep learning algorithms? 

Hemant: Deep learning algorithms are targeted at a variety of data and applications. For data, we have images, videos, text, and audio. For images, applications can be image classification, object detection, and so on. For textual data, applications are to translate the text or detect a sentiment of a text. For audio, the applications can be music generation, speech to text, and so on. 

05:08

Lois: It's important that we select the right deep learning algorithm based on the data and application, right? So how do we do that? 

Hemant: For image task like image classification, object detection, image segmentation, or facial recognition, CNN is a suitable architecture. For text, we have a choice of the latest transformers or LSTM or even RNN. For generative tasks like text summarization, question answering, transformers is a good choice. For generating images, text to image generation, transformers, GANs, or diffusion models are available choice.

05:51

Nikita: Let’s dive a little deeper into Artificial Neural Networks. Can you tell us more about them, Hemant? Hemant: Artificial Neural Networks are inspired by the human brain. They are made up of interconnected nodes called as neurons. 

Nikita: And how are inputs processed by a neuron? 

Hemant: In ANN, we assign weights to the connection between neurons. Weighted inputs are added up. And if the sum crosses a specified threshold, the neuron is fired. And the outputs of a layer of neuron become an input to another layer. 

06:27

Lois: Hemant, tell us about the building blocks of ANN so we understand this better.

Hemant: So first, building block is layers. We have input layer, output layer, and multiple hidden layers. The input layer and output layer are mandatory. And the hidden layers are optional. The second unit is neurons. Neurons are computational units, which accept an input and produce an output. 

Weights determine the strength of connection between neurons. So the connection could be between input and a neuron, or it could be between a neuron and another neuron. Activation functions work on the weighted sum of inputs to a neuron and produce an output. Additional input to the neuron that allows a certain degree of flexibility is called as a bias. 

07:27

Nikita: I think we’ve got the components of ANN straight but maybe you should give us an example. You mentioned this example earlier…of needing to train ANN to recognize handwritten digits from images. How would we go about that? Hemant: For that, we have to collect a large number of digit images, and we need to train ANN using these images.  So, in this case, the images consist of 28 by 28 pixels which act as input layer. For the output, we have neurons-- 10 neurons which represent digits 0 to 9. And we have multiple hidden layers. So, in this case, we have two hidden layers which are consisting of 16 neurons each. 

The hidden layers are responsible for capturing the internal representation of the raw image data. And the output layer is responsible for producing the desired outcomes. So, in this case, the desired outcome is the prediction of whether the digit is 0 or 1 or up to digit 9. 

So how do we train this particular ANN? So the first thing we use the backpropagation algorithm. During training, we show an image to the ANN. Let us say it is an image of digit 2. So we expect output neuron for digit 2 to fire. But in real, let us say output neuron of a digit 6 fired. 

09:12

Lois: So, then, what do we do? 

Hemant: We know that there is an error. So to correct an error, we adjust the weights of the connection between neurons based on a calculation, which we call as backpropagation algorithm. By showing thousands of images and adjusting the weights iteratively, ANN is able to predict correct outcome for most of the input images. This process of adjusting weights through backpropagation is called as model training. 

09:48

Do you have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us on the Idea Incubator. Your suggestion could find a place in future development projects! Visit mylearn.oracle.com to get started. 

10:09

Nikita: Welcome back! Let’s move on to CNN. Hemant, what is a Convolutional Neural Network? 

Hemant: CNN is a type of deep learning model specifically designed for processing and analyzing grid-like data, such as images and videos. In the ANN, the input image is converted to a single dimensional array and given as an input to the network.   But that does not work well with the image data because image data is inherently two dimensional. CNN works better with two dimensional data. The role of the CNN is to reduce the image into a form, which is easier to process and without losing features, which are critical for getting a good prediction. 

10:53

Lois: A CNN has different layers, right? Could you tell us a bit about them? 

Hemant: The first one is input layer. Input layer is followed by feature extraction layers, which is a combination and repetition of multiple feature extraction layers, including convolutional layer with ReLu activation and a pooling layer. 

And this is followed by a classification layer. These are the fully connected output layers, where the classification occurs as output classes. The feature extraction layers play a vital role in image classification.  

11:33

Nikita: Can you explain these layers with an example?

Hemant: Let us say we have a robot to inspect a house and tell us what type of a house it is. It uses many tools for this purpose. The first tool is a blueprint detector. It scans different parts of the house, like walls, floors, or windows, and looks for specific patterns or features. 

The second tool is a pattern highlighter. This tool marks areas detected by the blueprint detector. The next tool is a summarizer. It tries to capture the most significant features of every room. The next tool is house expert, which looks at all the highlighted patterns and features, and tries to understand the house. 

The next tool is a guess maker. It assigns probabilities to the different possible house types. And finally, the quality checker randomly checks different parts of the analysis to make sure that the robot doesn't rely too much on any single piece of information. 

12:40

Nikita: Ok, so how are you mapping these to the feature extraction layers? 

Hemant: Similar to blueprint detector, we have a convolutional layer. This layer applies convolutional operations to the input image using small filters known as kernels. 

Each filter slides across the input image to detect specific features, such as edges, corners, or textures. Similar to pattern highlighter, we have a activation function. The activation function allows the network to learn more complex and non-linear relationships in the data. Pooling layer is similar to room summarizer. 

Pooling helps reduce the spatial dimensions of the feature maps generated by the convolutional layers. Similar to house expert, we have a fully connected layer, which is responsible for making final predictions or classifications based on the learned features. Softmax layer converts the output of the last fully connected layers into probability scores. 

The class with the highest probability is the predicted class. This is similar to the guess maker. And finally, we have the dropout layer. This layer is a regularization technique used to prevent overfitting in the network. This has the same role as that of a quality checker. 

14:05

Lois: Do CNNs have any limitations that we need to be aware of?

Hemant: Training CNNs on large data sets can be computationally expensive and time consuming. CNNs are susceptible to overfitting, especially when the training data is limited or imbalanced. CNNs are considered black box models making it difficult to interpret. 

And CNNs can be sensitive to small changes in the input leading to unstable predictions. 

14:33

Nikita: And what are the top applications of CNN? Hemant: One of the most widely used applications of CNNs is image classification. For example, classifying whether an image contains a specific object, say cat or a dog. 

CNNs are used for object detection tasks. The goal here is to draw bounding boxes around objects in an image. CNNs can perform pixel level segmentation, where each pixel in the image is labeled to represent different objects or regions. CNNs are employed for face recognition tasks as well, identifying and verifying individuals based on facial features. 

CNNs are widely used in medical image analysis, helping with tasks like tumor detection, diagnosis, and classification of various medical conditions. CNNs play an important role in the development of self-driving cars, helping them to recognize and understand the road traffic signs, pedestrians, and other vehicles. And CNNs are applied in analyzing satellite images and remote sensing data for tasks, such as land cover classification and environmental monitoring. 

15:50

Nikita: Hemant, let’s talk about sequence models. What are they and what are they used for?

Hemant: Sequence models are used to solve problems, where the input data is in the form of sequences. The sequences are ordered lists of data points or events. 

The goal in sequence models is to find patterns and dependencies within the data and make predictions, classifications, or even generate new sequences. 

16:17

Lois: Can you give us some examples of sequence models? 

Hemant: Some common examples of the sequence models are in natural language processing, deep learning models are used for tasks, such as machine translation, sentiment analysis, or text generation. In speech recognition, deep learning models are used to convert a recorded audio into text. 

In deep learning models, can generate new music or create original compositions. Even sequences of hand gestures are interpreted by deep learning models for applications like sign language recognition. In fields like finance or weather prediction, time series data is used to predict future values. 

17:03

Nikita: Which deep learning models can be used to work with sequence data? 

Hemant: Recurrent Neural Networks, abbreviated as RNNs, are a class of neural network architectures specifically designed to handle sequential data. Unlike traditional feedforward neural network, RNNs have a feedback loop that allows information to persist across different timesteps. 

The key features of RNN is their ability to maintain an internal state often referred to as a hidden state or memory, which is updated as the network processes each element in the input sequence. The hidden state is then used as input to the network for the next time step, allowing the model to capture dependencies and patterns in the data that are spread across time. 

17:58

Nikita: Are there various types of RNNs?

Hemant: There are different types of RNN architecture based on application. 

One of them is one to one. This is like feed forward neural network and is not suited for sequential data. A one to many model produces multiple output values for one input value. Music generation or sequence generation are some applications using this architecture. 

A many to one model produces one output value after receiving multiple input values. Example is sentiment analysis based on the review. Many to many model produces multiple output values for multiple input values. Examples are machine translation and named entity recognition. 

RNN does not perform that well when it comes to capturing long term dependencies. This is due to the vanishing gradients problem, which is overcome by using LSTM model. 

19:07

Lois: Another acronym. What is LSTM, Hemant?

Hemant: Long Short-Term memory, abbreviated as LSTM, works by using a specialized memory cell and a gating mechanisms to capture long term dependencies in the sequential data.  The key idea behind LSTM is to selectively remember or forget information over time, enabling the model to maintain relevant information over long sequences, which helps overcome the vanishing gradients problem. 

19:40

Nikita: Can you take us, step-by-step, through the working of LSTM? 

Hemant: At each timestep, the LSTM takes an input vector representing the current data point in the sequence. The LSTM also receives the previous hidden state and cell state. These represent what the LSTM has remembered and forgotten up to the current point in the sequence. 

The core of the LSTM lies in its gating mechanisms, which include three gates: the input gate, the forget gate, and the output gate. These gates are like the filters that control the flow of information within the LSTM cell. The input gate decides what new information from the current input should be added to the memory cell. 

The forget gate determines what information in the current memory cell should be discarded or forgotten. The output gate regulates how much of the current memory cell should be exposed as the output of the current time step. Using the information from the input gate and forget gate, the LSTM updates its cell state. The LSTM then uses the output gate to produce the current hidden state, which becomes the output of the LSTM for the next time step. 

21:12

Lois: Thank you, Hemant, for joining us in this episode of the Oracle University Podcast. I learned so much today. If you want to learn more about deep learning, visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. And remember, the AI Foundations course and certification are free. So why not get started now?

Nikita: Right, Lois. In our next episode, we will discuss generative AI and language learning models. Until then, this is Nikita Abraham…

Lois: And Lois Houston signing off!

21:45

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

What is MySQL HeatWave? How do I get certified in it? Where do I start?

Listen to Lois Houston and Nikita Abraham, along with MySQL Developer Scott Stroz, answer all these questions and more on this week's episode of the Oracle University Podcast.

MySQL Document Store: https://oracleuniversitypodcast.libsyn.com/mysql-document-store

Oracle MyLearn: https://mylearn.oracle.com/

Oracle University Learning Community: https://education.oracle.com/ou-community

LinkedIn: https://www.linkedin.com/showcase/oracle-university/

X (formerly Twitter): https://twitter.com/Oracle_Edu

Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.

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Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this  series of informative podcasts, we’ll bring you foundational training on the most popular  Oracle technologies. Let’s get started!

00:26

Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hi there! For the last two weeks, we’ve been having really exciting discussions on everything AI. We covered the basics of artificial intelligence and machine learning, and we’re taking a short break from that today to talk about the new MySQL HeatWave Implementation Associate Certification with MySQL Developer Advocate Scott Stroz. 00:59 Nikita: You may remember Scott from an episode last year where he came on to discuss MySQL Document Store. We’ll post the link to that episode in the show notes so you can listen to it if you haven’t already.

Lois: Hi Scott! Thanks for joining us again. Before diving into the certification, tell us, what is MySQL HeatWave? 

01:19

Scott: Hi Lois, Hi Niki. I’m so glad to be back. So, MySQL HeatWave Database Service is a fully managed database that is capable of running transactional and analytic queries in a single database instance. This can be done across data warehouses and data lakes. We get all the benefits of analytic queries without the latency and potential security issues of performing standard extract, transform, and load, or ETL, operations. Some other MySQL HeatWave database service features are automated system updates and database backups, high availability, in-database machine learning with AutoML, MySQL Autopilot for managing instance provisioning, and enhanced data security. 

HeatWave is the only cloud database service running MySQL that is built, managed, and supported by the MySQL Engineering team.

02:14

Lois: And where can I find MySQL HeatWave?

Scott: MySQL HeatWave is only available in the cloud. MySQL HeatWave instances can be provisioned in Oracle Cloud Infrastructure or OCI, Amazon Web Services (AWS), and Microsoft Azure. Now, some features though are only available in Oracle Cloud, such as access to MySQL Document Store.

02:36

Nikita: Scott, you said MySQL HeatWave runs transactional and analytic queries in a single instance. Can you elaborate on that?

Scott: Sure, Niki. So, MySQL HeatWave allows developers, database administrators, and data analysts to run transactional queries (OLTP) and analytic queries (OLAP). 

OLTP, or online transaction processing, allows for real-time execution of database transactions. A transaction is any kind of insertion, deletion, update, or query of data. Most DBAs and developers work with this kind of processing in their day-to-day activities.   OLAP, or online analytical processing, is one way to handle multi-dimensional analytical queries typically used for reporting or data analytics. OLTP system data must typically be exported, aggregated, and imported into an OLAP system. This procedure is called ETL as I mentioned – extract, transform, and load. With large datasets, ETL processes can take a long time to complete, so analytic data could be “old” by the time it is available in an OLAP system. There is also an increased security risk in moving the data to an external source.

03:56

Scott: MySQL HeatWave eliminates the need for time-consuming ETL processes. We can actually get real-time analytics from our data since HeatWave allows for OLTP and OLAP in a single instance. I should note, this also includes analytic from JSON data that may be stored in the database.

Another advantage is that applications can use MySQL HeatWave without changing any of the application code. Developers only need to point their applications at the MySQL HeatWave databases. MySQL HeatWave is fully compatible with on-premise MySQL instances, which can allow for a seamless transition to the cloud.

And one other thing. When MySQL HeatWave has OLAP features enabled, MySQL can determine what type of query is being executed and route it to either the normal database system or the in-memory database.

04:52

Lois: That’s so cool! And what about the other features you mentioned, Scott? Automated updates and backups, high availability…

Scott: Right, Lois. But before that, I want to tell you about the in-memory query accelerator. MySQL HeatWave offers a massively parallel, in-memory hybrid columnar query processing engine. It provides high performance by utilizing algorithms for distributed query processing. And this query processing in MySQL HeatWave is optimized for cloud environments. 

MySQL HeatWave can be configured to automatically apply system updates, so you will always have the latest and greatest version of MySQL.

Then, we have automated backups. By this, I mean MySQL HeatWave can be configured to provide automated backups with point-in-time recovery to ensure data can be restored to a particular date and time. MySQL HeatWave also allows us to define a retention plan for our database backups, that means how long we keep the backups before they are deleted.

High availability with MySQL HeatWave allows for more consistent uptime. When using high availability, MySQL HeatWave instances can be provisioned across multiple availability domains, providing automatic failover for when the primary node becomes unavailable. All availability domains within a region are physically separated from each other to mitigate the possibility of a single point of failure.

06:14

Scott: We also have MySQL Lakehouse. Lakehouse allows for the querying of data stored in object storage in various formats. This can be CSV, Parquet, Avro, or an export format from other database systems. And basically, we point Lakehouse at data stored in Oracle Cloud, and once it’s ingested, the data can be queried just like any other data in a database. Lakehouse supports querying data up to half a petabyte in size using the HeatWave engine. And this allows users to take advantage of HeatWave for non-MySQL workloads.

MySQL AutoPilot is a part of MySQL HeatWave and can be used to predict the number of HeatWave nodes a system will need and automatically provision them as part of a cluster. AutoPilot has features that can handle automatic thread pooling and database shape predicting. A “shape” is one of the many different CPU, memory, and ethernet traffic configurations available for MySQL HeatWave.

MySQL HeatWave includes some advanced security features such as asymmetric encryption and automated data masking at query execution.

As you can see, there are a lot of features covered under the HeatWave umbrella! 07:31

Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started.  08:02

Nikita: Welcome back! Now coming to the certification, who can actually take this exam, Scott?

Scott: The MySQL HeatWave Implementation Associate Certification Exam is designed specifically for administrators and data scientists who want to provision, configure, and manage MySQL HeatWave for transactions, analytics, machine learning, and Lakehouse.

08:22

Nikita: Can someone who’s just graduated, say an engineering graduate interested in data analytics, take this certification? Are there any prerequisites? What are the career prospects for them?

Scott: There are no mandatory prerequisites, but anyone who wants to take the exam should have experience with MySQL HeatWave and other aspects of OCI, such as virtual cloud networks and identity and security processes. Also, the learning path on MyLearn will be extremely helpful when preparing for the exam, but you are not required to complete the learning path before registering for the exam.

The exam focuses more on getting MySQL HeatWave running (and keeping it running) than accessing the data. That doesn’t mean it is not helpful for someone interested in data analytics. I think it can be helpful for data analysts to understand how the system providing the data functions, even if it is at just a high level. It is also possible that data analysts might be responsible for setting up their own systems and importing and managing their own data.

09:23

Lois: And how do I get started if I want to get certified on MySQL HeatWave?

Scott: So, you’ll first need to go to mylearn.oracle.com and look for the “Become a MySQL HeatWave Implementation Associate” learning path. The learning path consists of over 10 hours of training across 8 different courses. 

These courses include “Getting Started with MySQL HeatWave Database Service,” which offers an introduction to some Oracle Cloud functionality such as security and networking, as well as showing one way to connect to a MySQL HeatWave instance. Another course demonstrates how to configure MySQL instances and copy that configuration to other instances. Other courses cover how to migrate data into MySQL HeatWave, set up and manage high availability, and configure HeatWave for OLAP.

You’ll find labs where you can perform hands-on activities, student and activity guides, and skill checks to test yourself along the way. And there’s also the option to Ask the Instructor if you have any questions you need answers to. You can also access the Oracle University Learning Community and discuss topics with others on the same journey. The learning path includes a practice exam to check your readiness to pass the certification exam.

10:33

Lois: Yeah, and remember, access to the entire learning path is free so there’s nothing stopping you from getting started right away. Now Scott, what does the certification test you on?

Scott: The MySQL HeatWave Implementation exam, which is an associate-level exam, covers various topics. It will validate your ability to identify key features and benefits of MySQL HeatWave and describe the MySQL HeatWave architecture; identify Virtual Cloud Network (VCN) requirements and the different methods of connecting to a MySQL HeatWave instance; manage the automatic backup process and restore database systems from these backups; configure and manage read replicas and inbound replication channels; import data into MySQL HeatWave; configure and manage high availability and clustering of MySQL HeatWave instances.

I know this seems like a lot of different topics. That is why we recommend anyone interested in the exam follow the learning path. It will help make sure you have the exposure to all the topics that are covered by the exam.

11:35

Lois: Tell us more about the certification process itself.

Scott: While the courses we already talked about are valuable when preparing for the exam, nothing is better than hands-on experience. We recommend that candidates have hands-on experience with MySQL HeatWave with real-world implementations. The format of the exam is Multiple Choice. It is 90 minutes long and consists of 65 questions. When you’ve taken the recommended training and feel ready to take the certification exam, you need to purchase the exam and register for it. You go through the section on things to do before the exam and the exam policies, and then all that’s left to do is schedule the date and time of the exam according to when is convenient for you.

12:16

Nikita: And once you’ve finished the exam?

Scott: When you’re done your score will be displayed on the screen when you finish the exam. You will also receive an email indicating whether you passed or failed. You can view your exam results and full score report in Oracle CertView, Oracle’s certification portal. From CertView, you can download and print your eCertificate and even share your newly earned badge on places like Facebook, Twitter, and LinkedIn.

12:38

Lois: And for how long does the certification remain valid, Scott?

Scott: There is no expiration date for the exam, so the certification will remain valid for as long as the material that is covered remains relevant. 

12:49

Nikita: What’s the next step for me after I get this certification? What other training can I take?

Scott: So, because this exam is an associate level exam, it is kind of a stepping stone along a person’s MySQL training. I do not know if there are plans for a professional level exam for HeatWave, but Oracle University has several other training programs that are MySQL-specific. There are learning paths to help prepare for the MySQL Database Administrator and MySQL Database Developer exams. As with the HeatWave learning paths, the learning paths for these exams include video tutorials, hands-on activities, skill checks, and practice exams.

13:27

Lois: I think you’ve told us everything we need to know about this certification, Scott. Are there any parting words you might have?

Scott: We know that the whole process of training and getting certified may seem daunting, but we’ve really tried to simplify things for you with the “Become a MySQL HeatWave Implementation Associate” learning path. It not only prepares you for the exam but also gives you experience with features of MySQL HeatWave that will surely be valuable in your career.

13:51

Lois: Thanks so much, Scott, for joining us today.

Nikita: Yeah, we’ve had a great time with you.

Scott: Thanks for having me.

Lois: Next week, we’ll get back to our focus on AI with a discussion on deep learning. Until then, this is Lois Houston…

Nikita: And Nikita Abraham, signing off.

14:07

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University  Podcast.

Does machine learning feel like too convoluted a topic? Not anymore!

Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work.

Oracle MyLearn: https://mylearn.oracle.com/

Oracle University Learning Community: https://education.oracle.com/ou-community

LinkedIn: https://www.linkedin.com/showcase/oracle-university/

X (formerly Twitter): https://twitter.com/Oracle_Edu

Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.

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Episode Transcript:

 

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this 

series of informative podcasts, we’ll bring you foundational training on the most popular 

Oracle technologies. Let’s get started! 

00:26

Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal 

Technical Editor.

Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning.

00:57

Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work?

Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data.

01:34

Nikita: Give us a few examples of machine learning… so we can see what it can do for us.

Hemant: Machine learning is used by all of us in our day-to-day life.

When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning.

We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning.

While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination.

02:24

Lois: So, how does machine learning actually work?

Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog.

Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data.

We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog.

Machine learning model is first trained with the data set. Training data set consists of a set of features and output labels, and is given as an input to the machine learning model.

During the process of training, machine learning model learns the relation between input features and corresponding output labels from the provided data. Once the model learns from the data, we have a trained model.

Once the model is trained, it can be used for inference. Inference is a process of getting a prediction by giving a data point. In this example, we input features of a cat or a dog, and the trained model predicts the output that is a cat or a dog label.

The types of machine learning models depend on whether we have a labeled output or not.

04:08

Nikita: Oh, there are different types of machine learning models?

Hemant: In general, there are three types of machine learning approaches.

In supervised machine learning, labeled data is used to train the model. Model learns the relation between features and labels.

Unsupervised learning is generally used to understand relationships within a data set. Labels are not used or are not available.

Reinforcement learning uses algorithms that learn from outcomes to make decisions or choices.

04:45

Lois: Ok…supervised learning, unsupervised learning, and reinforcement learning. Where do we use each of these machine learning models?

Hemant: Some of the popular applications of supervised machine learning are disease detection, weather forecasting, stock price prediction, spam detection, and credit scoring. For example, in disease detection, the patient data is input to a machine learning model, and machine learning model predicts if a patient is suffering from a disease or not.

For unsupervised machine learning, some of the most common real-time applications are to detect fraudulent transactions, customer segmentation, outlier detection, and targeted marketing campaigns. So for example, given the transaction data, we can look for patterns that lead to fraudulent transactions.

Most popular among reinforcement learning applications are automated robots, autonomous driving cars, and playing games.

05:51

Nikita: I want to get into how each type of machine learning works. Can we start with supervised learning?

Hemant: Supervised learning is a machine learning model that learns from labeled data. The model learns the mapping between the input and the output.

As a house price predictor model, we input house size in square feet and model predicts the price of a house. Suppose we need to develop a machine learning model for detecting cancer, the input to the model would be the person's medical details, the output would be whether the tumor is malignant or not.

06:29

Lois: So, that mapping between the input and output is fundamental in supervised learning.

Hemant: Supervised learning is similar to a teacher teaching student. The model is trained with the past outcomes and it learns the relationship or mapping between the input and output.

In supervised machine learning model, the outputs can be either categorical or continuous. When the output is continuous, we use regression. And when the output is categorical, we use classification.

07:05

Lois: We want to keep this discussion at a high level, so we’re not going to get into regression and classification. But if you want to learn more about these concepts and look at some demonstrations, visit mylearn.oracle.com.

Nikita: Yeah, look for the Oracle Cloud Infrastructure AI Foundations course and you’ll find a lot of resources that you can make use of.

07:30

The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community.

Visit mylearn.oracle.com to get started. 

07:58

Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Hemant?

Hemant: Unsupervised machine learning is a type of machine learning where there are no labeled outputs. The algorithm learns the patterns and relationships in the data and groups similar data items. In unsupervised machine learning, the patterns in the data are explored explicitly without being told what to look for.

For example, if you give a set of different-colored LEGO pieces to a child and ask to sort it, it may the LEGO pieces based on any patterns they observe. It could be based on same color or same size or same type. Similarly, in unsupervised learning, we group unlabeled data sets.

One more example could be-- say, imagine you have a basket of various fruits-- say, apples, bananas, and oranges-- and your task is to group these fruits based on their similarities. You observe that some fruits are round and red, while others are elongated and yellow. Without being told explicitly, you decide to group the round and red fruits together as one cluster and the elongated and yellow fruits as another cluster. There you go. You have just performed an unsupervised learning task.

09:21

Lois: Where is unsupervised machine learning used? Can you take us through some use cases?

Hemant: The first use case of unsupervised machine learning is market segmentation. In market segmentation, one example is providing the purchasing details of an online shop to a clustering algorithm. Based on the items purchased and purchasing behavior, the clustering algorithm can identify customers based on the similarity between the products purchased. For example, customers with a particular age group who buy protein diet products can be shown an advertisement of sports-related products.

The second use case is on outlier analysis. One typical example for outlier analysis is to provide credit card purchase data for clustering. Fraudulent transactions can be detected by a bank by using outliers. In some transaction, amounts are too high or recurring. It signifies an outlier.

The third use case is recommendation systems. An example for recommendation systems is to provide users' movie viewing history as input to a clustering algorithm. It clusters users based on the type or rating of movies they have watched. The output helps to provide personalized movie recommendations to users. The same applies for music recommendations also.

10:53

Lois: And finally, Hemant, let’s talk about reinforcement learning.

Hemant: Reinforcement learning is like teaching a dog new tricks. You reward it when it does something right, and over time, it learns to perform these actions to get more rewards. Reinforcement learning is a type of Machine Learning that enables an agent to learn from its interaction with the environment, while receiving feedback in the form of rewards or penalties without any labeled data.

Reinforcement learning is more prevalent in our daily lives than we might realize. The development of self-driving cars and autonomous drones rely heavily on reinforcement learning to make real time decisions based on sensor data, traffic conditions, and safety considerations.

Many video games, virtual reality experiences, and interactive entertainment use reinforcement learning to create intelligent and challenging computer-controlled opponents. The AI characters in games learn from player interactions and become more difficult to beat as the game progresses.

12:05

Nikita: Hemant, take us through some of the terminology that’s used with reinforcement learning.

Hemant: Let us say we want to train a self-driving car to drive on a road and reach its destination. For this, it would need to learn how to steer the car based on what it sees in front through a camera. Car and its intelligence to steer on the road is called as an agent.

More formally, agent is a learner or decision maker that interacts with the environment, takes actions, and learns from the feedback received. Environment, in this case, is the road and its surroundings with which the car interacts. More formally, environment is the external system with which the agent interacts. It is the world or context in which the agent operates and receives feedback for its actions.

What we see through a camera in front of a car at a moment is a state. State is a representation of the current situation or configuration of the environment at a particular time. It contains the necessary information for the agent to make decisions. The actions in this example are to drive left, or right, or keep straight. Actions are a set of possible moves or decisions that the agent can take in a given state.

Actions have an impact on the environment and influence future states. After driving through the road many times, the car learns what action to take when it views a road through the camera. This learning is a policy. Formally, policy is a strategy or mapping that the agent uses to decide which action to take in a given state. It defines the agent's behavior and determines how it selects actions.

13:52

Lois: Ok. Say we’re talking about the training loop of reinforcement learning in the context of training a dog to learn tricks. We want it to pick up a ball, roll, sit…

Hemant: Here the dog is an agent, and the place it receives training is the environment. While training the dog, you provide a positive reward signal if the dog picks it right and a warning or punishment if the dog does not pick up a trick. In due course, the dog gets trained by the positive rewards or negative punishments.

The same tactics are applied to train a machine in the reinforcement learning. For machines, the policy is the brain of our agent. It is a function that tells what actions to take when in a given state. The goal of reinforcement learning algorithm is to find a policy that will yield a lot of rewards for the agent if the agent follows that policy referred to as the optimal policy.

Through a process of learning from experiences and feedback, the agent becomes more proficient at making good decisions and accomplishing tasks. This process continues until eventually we end up with the optimal policy. The optimal policy is learned through training by using algorithms like Deep Q Learning or Q Learning.

15:19

Nikita: So through multiple training iterations, it gets better. That’s fantastic. Thanks, Hemant, for joining us today. We’ve learned so much from you.

Lois: Remember, the course and certification are free, so if you’re interested, make sure you log in to mylearn.oracle.com and get going. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston…

Nikita: And Nikita Abraham signing off!

15:48

 

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

You probably interact with artificial intelligence (AI) more than you realize. So, there’s never been a better time to start figuring out how it all works.   Join Lois Houston and Nikita Abraham as they decode the fundamentals of AI so that anyone, irrespective of their technical background, can leverage the benefits of AI and tap into its infinite potential.   Together with Senior Cloud Engineer Nick Commisso, they take you through key AI concepts, common AI tasks and domains, and the primary differences between AI, machine learning, and deep learning.   Oracle MyLearn: https://mylearn.oracle.com/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26

Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hi there! Welcome to a new season of the Oracle University Podcast. I’m so excited about this season because we’re going to delve into the world of artificial intelligence. In upcoming episodes, we’ll talk about the fundamentals of artificial intelligence and machine learning. And we’ll discuss neural network architectures, generative AI and large language models, the OCI AI stack, and OCI AI services.

01:06

Nikita: So, if you’re an IT professional who wants to start learning about AI and ML or even if you’re a student who is familiar with OCI or similar cloud services, but have no prior exposure to this field, you’ll want to tune in to these episodes. Lois: That’s right, Niki. So, let’s get started. Today, we’ll talk about the basics of artificial intelligence with Senior Cloud Engineer Nick Commisso. Hi Nick! Thanks for joining us today. So, let’s start right at the beginning. What is artificial intelligence?

01:36 Nick: Well, the ability of machines to imitate the cognitive abilities and problem solving capabilities of human intelligence can be classified as artificial intelligence or AI. 

01:47

Nikita: Now, when you say capabilities and abilities, what are you referring to?

Nick: Human intelligence is the intellectual capability of humans that allows us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using a language and understand the nonverbal cues, such as facial recognition, tone variation, and body language. 

You can handle objections in real time, even in a complex setting. You can plan for short and long-term situations or projects. And, of course, you can create music and art or invent something new like an original idea. 

If you can replicate any of these human capabilities in machines, this is artificial general intelligence or AGI. So in other words, AGI can mimic human sensory and motor skills, performance, learning, and intelligence, and use these abilities to carry out complicated tasks without human intervention. 

When we apply AGI to solve problems with specific and narrow objectives, we call it artificial intelligence or AI. 

02:55

Lois: It seems like AI is everywhere, Nick. Can you give us some examples of where AI is used?

Nick: AI is all around us, and you've probably interacted with AI, even if you didn't realize it. Some examples of AI can be viewing an image or an object and identifying if that is an apple or an orange. It could be examining an email and classifying it spam or not. It could be writing computer language code or predicting the price of an older car. 

So let's get into some more specifics of AI tasks and the nature of related data. Machine learning, deep learning, and data science are all associated with AI, and it can be confusing to distinguish. 

03:36

Nikita: Why do we need AI? Why’s it important? 

Nick: AI is vital in today's world, and with the amount of data that's generated, it far exceeds the human ability to absorb, interpret, and actually make decisions based on that data. That's where AI comes in handy by enhancing the speed and effectiveness of human efforts. 

So here are two major reasons why we need AI. Number one, we want to eliminate or reduce the amount of routine tasks, and businesses have a lot of routine tasks that need to be done in large numbers. So things like approving a credit card or a bank loan, processing an insurance claim, recommending products to customers are just some example of routine tasks that can be handled. 

And second, we, as humans, need a smart friend who can create stories and poems, designs, create code and music, and have humor, just like us. 

04:33

Lois: I’m onboard with getting help from a smart friend! There are different domains in AI, right, Nick? 

Nick: We have language for language translation; vision, like image classification; speech, like text to speech; product recommendations that can help you cross-sell products; anomaly detection, like detecting fraudulent transactions; learning by reward, like self-driven cars. You have forecasting with weather forecasting. And, of course, generating content like image from text. 

05:03

Lois: There are so many applications. Nick, can you tell us more about these commonly used AI domains like language, audio, speech, and vision?

Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, or extracting key phrases and so on. 

Consider the example of translating text. There's many text translation tools where you simply type or paste your text into a given text box, choose your source and target language, and then click translate. 

Now, let's look at the generative AI tasks. They are generative, which means the output text is generated by a model. Some examples are creating text like stories or poems, summarizing a text, answering questions, and so on. Let's take the example of ChatGPT, the most well-known generative chat bot. These bots can create responses from their training on large language models, and they continuously grow through machine learning. 

06:10

Nikita: What can you tell us about using text as data?

Nick: Text is inherently sequential, and text consists of sentences. Sentences can have multiple words, and those words need to be converted to numbers for it to be used to train language models. This is called tokenization. Now, the length of sentences can vary, and all the sentences lengths need to be made equal. This is done through padding. 

Words can have similarities with other words, and sentences can also be similar to other sentences. The similarity can be measured through dot similarity or cosine similarity. We need a way to indicate that similar words or sentences may be close by. This is done through representation called embedding. 

06:56 Nikita: And what about language AI models?

Nick: Language AI models refer to artificial intelligence models that are specifically designed to understand, process, and generate natural language. These models have been trained on vast amounts of textual data that can perform various natural language processing or NLP tasks. 

The task that needs to be performed decides the type of input and output. The deep learning model architectures that are typically used to train models that perform language tasks are recurrent neural networks, which processes data sequentially and stores hidden states, long short-term memory, which processes data sequentially that can retain the context better through the use of gates, and transformers, which processes data in parallel. It uses the concept of self-attention to better understand the context. 

07:48

Lois: And then there’s speech-related AI, right?

Nick: Speech-related AI tasks can be either audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech-to-text conversion or speaker recognition, voice conversion, and so on. Generative AI tasks are generative in nature, so the output audio is generated by a model. For example, you have music composition and speech synthesis.  Audio or speech is digitized as snapshots taken in time. The sample rate is the number of times in a second an audio sample is taken. Most digital audio have a sampling rate of 44.1 kilohertz, which is also the sampling rate for audio CDs.  Multiple samples need to be correlated to make sense of the data. For example, listening to a song for a fraction of a second, you won't be able to infer much about the song, and you'll probably need to listen to it a little bit longer. 

Audio and speech AI models are designed to process and understand audio data, including spoken language. These deep-learning model architectures are used to train models that perform language with tasks-- recurrent neural networks, long short-term memory, transformers, variational autoencoders, waveform models, and Siamese networks. All of the models take into consideration the sequential nature of audio. 

09:21 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 

09:49

Nikita: Welcome back! Now that we’ve covered language and speech-related tasks, let’s move on to vision-related tasks. Nick: Vision-related AI tasks could be image related or generative AI. Image-related AI tasks will use an image as an input, and the output depends on the task. Some examples are classifying images, identifying objects in an image, and so on. Facial recognition is one of the most popular image-related tasks that is often used for surveillance and tracking of people in real time, and it's used in a lot of different fields, including security, biometrics, law enforcement, and social media.  For generative AI tasks, the output image is generated by a model. For example, creating an image from a contextual description, generating images of a specific style or a high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of an object, machine components, buildings, medication, people, and even more. 

10:53

Lois: So, then, here again I need to ask, how do images work as data?

Nick: Images consist of pixels, and pixels can be either grayscale or color. And we can't really make out what an image is just by looking at one pixel. 

The task that needs to be performed decides the type of input needed and the output produced. Various architectures have evolved to handle this wide variety of tasks and data. These deep-learning model architectures are typically used to train models that perform vision tasks-- convolutional neural networks, which detects patterns in images; learning hierarchical representations of visual features; YOLO, which is You Only Look Once, processes the image and detects objects within the image; and then you have generative adversarial networks, which generates real-looking images. 

11:43

Nikita: Nick, earlier you mentioned other AI tasks like anomaly detection, recommendations, and forecasting. Could you tell us more about them?

Nick: Anomaly detection. This is time-series data, which is required for anomaly detection, and it can be a single or multivariate for fraud detection, machine failure, etc.  Recommendations. You can recommend products using data of similar products or users. For recommendations, data of similar products or similar users is required. 

Forecasting. Time-series data is required for forecasting and can be used for things like weather forecasting and predicting the stock price. 

12:22

Lois: Nick, help me understand the difference between artificial intelligence, machine learning, and deep learning. Let’s start with AI. 

Nick: Imagine a self-driving car that can make decisions like a human driver, such as navigating traffic or detecting pedestrians and making safe lane changes. AI refers to the broader concept of creating machines or systems that can perform tasks that typically require human intelligence. Next, we have machine learning or ML. Visualize a spam email filter that learns to identify and move spam emails to the spam folder, and that's based on the user's interaction and email content. Now, ML is a subset of AI that focuses on the development of algorithms that enable machines to learn from and make predictions or decisions based on data. 

To understand what an algorithm is in the context of machine learning, it refers to a specific set of rules, mathematical equations, or procedures that the machine learning model follows to learn from data and make predictions on. And finally, we have deep learning or DL. Think of an image recognition software that can identify specific objects or animals within images, such as recognizing cats in photos on the internet. DL is a subfield of ML that uses neural networks with many layers, deep neural networks, to learn and make sense of complex patterns in data. 

13:51

Nikita: Are there different types of machine learning?

Nick: There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning where the algorithm learns from labeled data, making predictions or classifications. Unsupervised learning is an algorithm that discovers patterns and structures in unlabeled data, such as clustering or dimensionality reduction. And then, you have reinforcement learning, where agents learn to make predictions and decisions by interacting with an environment and receiving rewards or punishments. 

14:27

Lois: Can we do a deep dive into each of these types you just mentioned? We can start with the supervised machine learning algorithm.

Nick: Let's take an example of how a credit card company would approve a credit card. Once the application and documents are submitted, a verification is done, followed by a credit score check and another 10 to 15 days for approval. And how is this done? Sometimes, purely manually or by using a rules engine where you can build rules, give new data, get a decision.  The drawbacks are slow. You need skilled people to build and update rules, and the rules keep changing. The good thing is that the businesses had a lot of insight as to how the decisions were made. Can we build rules by looking at the past data?  We all learn by examples. Past data is nothing but a set of examples. Maybe reviewing past credit card approval history can help. Through a process of training, a model can be built that will have a specific intelligence to do a specific task. The heart of training a model is an algorithm that incrementally updates the model by looking at the data samples one by one.  And once it's built, the model can be used to predict an outcome on a new data. We can train the algorithm with credit card approval history to decide whether to approve a new credit card. And this is what we call supervised machine learning. It's learning from labeled data. 

15:52

Lois: Ok, I see. What about the unsupervised machine learning algorithm?

Nick: Data does not have a specific outcome or a label as we know it. And sometimes, we want to discover trends that the data has for potential insights. Similar data can be grouped into clusters. For example, retail marketing and sales, a retail company may collect information like household size, income, location, and occupation so that the suitable clusters could be identified, like a small family or a high spender and so on. And that data can be used for marketing and sales purposes.  Regulating streaming services. A streaming service may collect information like viewing sessions, minutes per session, number of unique shows watched, and so on. That can be used to regulate streaming services. Let's look at another example. We all know that fruits and vegetables have different nutritional elements. But do we know which of those fruits and vegetables are similar nutritionally? 

For that, we'll try to cluster fruits and vegetables' nutritional data and try to get some insights into it. This will help us include nutritionally different fruits and vegetables into our daily diets. Exploring patterns and data and grouping similar data into clusters drives unsupervised machine learning. 

17:13

Nikita: And then finally, we come to the reinforcement learning algorithm. 

Nick: How do we learn to play a game, say, chess? We'll make a move or a decision, check to see if it's the right move or feedback, and we'll keep the outcomes in your memory for the next step you take, which is learning. Reinforcement learning is a machine learning approach where a computer program learns to make decisions by trying different actions and receiving feedback. It teaches agents how to solve tasks by trial and error. This approach is used in autonomous car driving and robots as well. 

17:46

Lois: We keep coming across the term “deep learning.” You’ve spoken a bit about it a few times in this episode, but what is deep learning, really? How is it related to machine learning?

Nick: Deep learning is all about extracting features and rules from data. Can we identify if an image is a cat or a dog by looking at just one pixel? Can we write rules to identify a cat or a dog in an image? Can the features and rules be extracted from the raw data, in this case, pixels? 

Deep learning is really useful in this situation. It's a special kind of machine learning that trains super smart computer networks with lots of layers. And these networks can learn things all by themselves from pictures, like figuring out if a picture is a cat or a dog. 

18:28

Lois: I know we’re going to be covering this in detail in an upcoming episode, but before we let you go, can you briefly tell us about generative AI?

Nick: Generative AI, a subset of machine learning, creates diverse content like text, audio, images, and more. These models, often powered by neural networks, learn patterns from existing data to craft fresh and creative output. For instance, ChatGPT generates text-based responses by understanding patterns in text data that it's been trained on. Generative AI plays a vital role in various AI tasks requiring content creation and innovation. 

19:07

Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. As you complete the course, you’ll find skill checks that you can attempt to solidify your learning. 

Lois: And remember, the AI Foundations course on MyLearn also prepares you for the Oracle Cloud Infrastructure 2023 AI Foundations Associate certification. Both the course and the certification are free, so there’s really no reason NOT to take the leap into AI, right Niki?

Nikita: That’s right, Lois!

Lois: In our next episode, we will look at the fundamentals of machine learning. Until then, this is Lois Houston…

Nikita: And Nikita Abraham signing off!

19:52

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

How do I get certified in Oracle Autonomous Database? What material can I use to prepare for it? What's the exam like? How long is the certification valid for?   If these questions have been keeping you up at night, then join Lois Houston and Nikita Abraham in their conversation with Senior Principal OCI Instructor Susan Jang to understand the process of getting certified and begin your learning adventure.   Oracle MyLearn: mylearn.oracle.com/ Oracle University Learning Community: education.oracle.com/ou-community LinkedIn: linkedin.com/showcase/oracle-university/ X (formerly Twitter): twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript  

00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor.

Nikita: Hi everyone! If you’ve listened to us these last few weeks, you’ll know we’ve been discussing Oracle Autonomous Database in detail. We looked at Autonomous Database on serverless and dedicated infrastructure.

00:51

Lois: That’s right, Niki. Then, last week, we explored Autonomous Database tools. Today, we thought we’d wrap up our focus on Autonomous Database by talking about the training offered by Oracle University, the associated certification, how to prepare for it, what you should do next, and more.

Nikita: Yeah, we’ll get answers to all the big questions. And we’re going to get them from Susan Jang. Sue is a Senior Principal OCI Instructor with Oracle University. She has created and delivered training in Oracle databases and Oracle Cloud Infrastructure for over 20 years. Hi Sue! Thanks for joining us today.

Sue: Happy to be here!

01:29

Lois: Sue, what training does Oracle have on Autonomous Database?   Sue: Oracle University offers a professional-level course called the Oracle Autonomous Database Administration Workshop. So, if you want to learn to deploy and administer autonomous databases, this is the one for you. You’ll explore the fundamentals of the autonomous databases, their features, and benefits. You’ll learn about the technical architecture, the tasks that are involved in creating an autonomous database on a shared and on a dedicated Exadata infrastructure. You’ll discover what is the Machine Learning, you’ll discover what is APEX, which is Application Express, and SQL Developer Web, which is all deployed with the Autonomous Database. So basically everything you need to take your skills to the next level and become a proficient database administrator is in this course.

02:28

Nikita: Who can take this course, Sue?    Sue: The course is really for anyone interested in Oracle Autonomous Database, whether you’re a database administrator, a cloud data management professional, or a consultant.

The topics in the course include everything from the features of an Autonomous Database through provisioning, managing, and monitor of the database.

Most people think that just because it is an Autonomous Database, Oracle will do everything for you, and there is nothing a DBA can do or needs to do. But that’s not true.   An Oracle Autonomous Database automates the day-to-day DBA tasks, like tuning the database to ensure it is running at performance level or that the backups are done successfully. By letting the Autonomous Database perform those tasks, it gives the database administrator time to fully understand the new features of an Oracle database and figure out how to implement the features that will benefit the DBA’s company.

03:30

Lois: Would a non-database administrator benefit from taking this course?   Sue: Yes, Lois. Oracle courses are designed in modules, so you can focus on the modules that meet your needs. For example, if you’re a senior technical manager, you may not need to manage and monitor the Autonomous Database. But still, it’s important to understand its features and architecture to know how other Oracle products integrate with the database.

03:57

Nikita: Right. Talking about the course itself, each module consists of videos that teach different concepts, right?

Sue: Yes, Niki. Each video covers one topic. A group of topics, or I should say a group of related topics, makes up a module.

We know your time is important to you, and your success is important to us. You don’t just want to spend time taking training. You want to know that you’re really understanding the concepts of what you are learning. 

So to help you do this, we have skill checks at the end of most modules. You must successfully answer 80% of the questions to pass these knowledge checks. These checks are an excellent way to ensure that you’re on the right track and have the understanding of each module before you move on to the next one. 

04:48   Lois: That’s great. And are there any other resources to help reinforce what’s been learned?   Sue: I grew up with this phrase from my Mom. Education was her career. I remember hearing, “I hear and I forget. I see and I remember. I do and I understand.” It’s important to us that you understand the concepts and can actually “do” or “perform” the tasks. 

You'll find several demos in the different modules of the Autonomous Database Administration Workshop. These videos are where the instructor shows you how to perform the tasks so you can reinforce what you learned in the lessons. You’ll find demos on provisioning an autonomous database, creating an autonomous database clone, and configuring disaster recovery, and lots more.   Oracle also has what we call LiveLabs. These are a series of hands-on tutorials with step-by-step instructions to guide you through performing the tasks.

05:49

Nikita: I love the idea of LiveLabs. You can follow instructions on how to perform administrative tasks and then practice doing that on your own.

Lois: Yeah, that’s fantastic. OK Sue, say I’ve taken the course. What do I do next? 

Sue: Well, after you’ve taken the course, you’ll want to demonstrate your expertise with a certification. Because you want to get that better job. You want to increase your earning potential. You need to take the certification called the Oracle Autonomous Database Cloud Professional.

We have a couple of resources to help you along the way to ensure you succeed in securing that certification.

In MyLearn, the Oracle University online learning platform, you’ll see that the course, Oracle Autonomous Database Administration Workshop, falls within a learning path called Become an Oracle Autonomous Database Cloud Professional. The course is the first section of this learning path. The next section is a video describing the certification exam and how to prepare for it. The section after that is a practice exam. Now, though it doesn’t have the actual questions, you’ll find the exam will give you a good idea of the type of questions that will be asked in the exam. 

07:10

Lois: OK, so now I’ve done all that, and I’m ready to validate my knowledge and expertise. Tell me more about the certification, Sue.

Sue: To get the certification, you must take an online exam. The duration of the exam is 90 minutes. It’s a Multiple Choice format, and there are 60 questions to the exam. 

By getting this certification, you’re demonstrating to the world that you have the knowledge to provision, manage, and monitor, as well as migrate workloads to the Autonomous Database, on both a shared as well as a dedicated Exadata infrastructure. You will show you have the understanding of the architect of the Autonomous Database and can successfully use the features as well as its workflow, and you are capable of using Autonomous Database tools in developing an Autonomous Database.

08:05

Nikita: Great! So what do I need to do to take the exam? Sue: We assume you’ve already taken the course (making sure that you’re up to date with the training), that you’ve taken the time to study the topics in depth rather than memorizing superficial information just to pass the exam, looked at the available preparation material, and you’ve also taken the practice exam. I highly recommend that you have the hands-on experience or practice on an Autonomous Database before you take the certification exam.

08:38 Nikita: Hold on, Sue. You said to make sure we’re up to date with the training. How do I do that?

Sue: Technology is ever-changing, and at Oracle, we continually enhance our products to provide features that make them faster or more straightforward to use. So, if you’re taking a course, you may find a small tag that says “New” next to a topic. That indicates that there are some new training that’s been added to the course. So what I’m trying to say is if you’re looking to take some certification, check the course before you register for the exam and to see if there are any “New” tags. If you find them, you can learn what’s new and not have to go through the entire course again. This way, you’re up to date with the training!

09:25

Nikita: Ok. Got it. Tell us more about the certification, Sue. Sue: If you’re ready, search for the Become An Oracle Autonomous Database Cloud Professional learning path in MyLearn and scroll down to the Oracle Database Cloud Professional exam. Click on the “Register Now” button. You’ll be taken to a page where you’ll see the exam overview, the resources to help you prepare for the exam, a button to register for the exam, and things to do before your exam session. It will also describe what happens after the exam and some exam policies, like what to do if you need to reschedule your exam. When you’re ready to take the exam, you can schedule the date and time according to when it’s convenient for you. 

10:15

Lois: What’s the actual experience of taking the exam like?

Sue: It’s pretty straightforward. You want to prepare your system a day or two before the exam. You want to ensure you can connect successfully to the test site and that your laptop is plugged in and not running on battery. You want to make sure all other applications are closed before you perform the system test. Now, the system test is really with the test site and consists of testing your microphone, an internet speed test, and your video.

You will also be asked to do a test-exam simulation. You will need to be able to download the simulation exam and answer a few simple true or false questions. Once you have successfully done that, you’re ready to take the test on your laptop on the actual day of the test.

Now, on the day of the test, set up your test environment. For your test environment, what it really entails is that you have an environment that you do not have anything on your desk. You cannot have a second monitor. And it’s best to have a clear wall behind you so that the proctor can see there is nothing around you. And don’t forget to turn off your mobile device.

11:34

Lois: Ok, I’ve taken the test, and I passed. Wohoo! What happens now?

Sue: When you pass the exam, you will receive an email from Oracle with your results as well as a link to Oracle CertView. This is the Oracle certification candidate portal. In CertView, you can download and print your eCertificate. You can share your newly earned badge on places like Facebook, Twitter, and LinkedIn, or even email your employer and others a secure link that they can use to confirm and validate your credentials.

12:11

Nikita: Can anyone take the certification?

Sue: Yes, Niki. This certification is available to all candidates, including on-premise database administrators, cloud data management professionals, and consultants.

12:24

Lois: How long is the certification valid? What happens when it expires?

Sue: Certain Oracle credentials require periodic recertification for Oracle to recognize them as "active." For such credentials, you must upgrade to a current version within 12 months following the Oracle credential retirement to keep your certification active.

12:51

Are you planning to become an Oracle Certified Professional this year? Whether you're a seasoned IT pro or just starting your career, getting certified can give you a significant boost. And don't worry, we've got your back. Join us at one of our cert prep live events in the Oracle University Learning Community. You'll get insider tips from seasoned experts and learn from other professionals' experiences. Plus, once you've earned your certification, you'll become part of our exclusive forum for Oracle-certified users. So, what are you waiting for? Head over to mylearn.oracle.com and create an account to jump-start your journey towards certification today!

13:35

Nikita: Welcome back. Sue, what other training can I take after Autonomous Database?    Sue: Now that you have a strong foundation in the database, there is so much more that you can learn in Oracle. You can consider Exadata if you work on a high-performance data workload that’s running mission-critical applications. Look for a learning path called Become an Exadata Service Cloud Administrator, in MyLearn, to help you with that. GoldenGate is also a good choice if you work with data that needs to be shared and replicated, both locally as well as globally. The course for this is called Oracle GoldenGate 19c: Administration/Implementation.  

A hot topic in technology today is generative AI (Artificial Intelligence). You want to learn how to implement data security on different levels when it needs to be shared with large language model providers. 

Perhaps venture beyond the database and learn about Oracle Cloud Infrastructure and how its components and the many cloud services work together. Just go to mylearn.oracle.com, and in the field where you see “What do you want to learn?” type in what interests you and let your learning adventure begin!

14:59

Lois: And since you brought up AI, Sue, this is the perfect time to mention that we’ll be focusing on it for the next couple of weeks. We’ll be speaking to some of our colleagues on topics like artificial intelligence, machine learning, deep learning, generative AI, the OCI AI portfolio and more, but we’ll talk more about that next week.

Nikita: Yeah, can’t wait for that. Thank you so much, Sue, for giving us your time today.

Sue: Thanks for having me!

Lois: Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

15:29

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click  Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate  and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In this episode, hosts Lois Houston and Nikita Abraham speak with Oracle Database experts about the various tools you can use with Autonomous Database, including Oracle Application Express (APEX), Oracle Machine Learning, and more.   Oracle MyLearn: https://mylearn.oracle.com/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Tamal Chatterjee, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26

Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is

Nikita Abraham, Principal Technical Editor.

Nikita: Hi everyone! We spent the last two episodes exploring Oracle Autonomous Database’s deployment options: Serverless and Dedicated. Today, it’s tool time!

Lois: That’s right, Niki. We’ll be chatting with some of our Database experts on the tools that you can use with the Autonomous Database. We’re going to hear from Patrick Wheeler, Kay Malcolm, Sangeetha Kuppuswamy, and Thea Lazarova.

Nikita: First up, we have Patrick, to take us through two important tools. Patrick, let’s start with Oracle Application Express. What is it and how does it help developers?

01:15

Patrick: Oracle Application Express, also known as APEX-- or perhaps APEX, we're flexible like that-- is a low-code development platform that enables you to build scalable, secure, enterprise apps with world-class features that can be deployed anywhere. Using APEX, developers can quickly develop and deploy compelling apps that solve real problems and provide immediate value. You don't need to be an expert in a vast array of technologies to deliver sophisticated solutions. Focus on solving the problem, and let APEX take care of the rest.

01:52

Lois: I love that it’s so easy to use. OK, so how does Oracle APEX integrate with Oracle Database? What are the benefits of using APEX on Autonomous Database?

Patrick: Oracle APEX is a fully supported, no-cost feature of Oracle Database. If you have Oracle Database, you already have Oracle APEX. You can access APEX from database actions. Oracle APEX on Autonomous Database provides a preconfigured, fully managed, and secure environment to both develop and deploy world-class applications. Oracle takes care of configuration, tuning, backups, patching, encryption, scaling, and more, leaving you free to focus on solving your business problems. APEX enables your organization to be more agile and develop solutions faster for less cost and with greater consistency. You can adapt to changing requirements with ease, and you can empower professional developers, citizen developers, and everyone else.

02:56

Nikita: So you really don’t need to have a lot of specializations or be an expert to use APEX. That’s so cool! Now, what are the steps involved in creating an application using APEX? 

Patrick: You will be prompted to log in as the administrator at first. Then, you may create workspaces for your respective users and log in with those associated credentials. Application Express provides you with an easy-to-use, browser-based environment to load data, manage database objects, develop REST interfaces, and build applications which look and run great on both desktop and mobile devices.

You can use APEX to develop a wide variety of solutions, import spreadsheets, and develop a single source of truth in minutes. Create compelling data visualizations against your existing data, deploy productivity apps to elegantly solve a business need, or build your next mission-critical data management application. There are no limits on the number of developers or end users for your applications.

04:01

Lois: Patrick, how does APEX use SQL? What role does SQL play in the development of APEX applications? 

Patrick: APEX embraces SQL. Anything you can express with SQL can be easily employed in an APEX application. Application Express also enables low-code development, providing developers with powerful data management and data visualization components that deliver modern, responsive end user experiences out-of-the-box. Instead of writing code by hand, you're able to use intelligent wizards to guide you through the rapid creation of applications and components.

Creating a new application from APEX App Builder is as easy as one, two, three. One, in App Builder, select a project name and appearance. Two, add pages and features to the app. Three, finalize settings, and click Create.

05:00

Nikita: OK. So, the other tool I want to ask you about is Oracle Machine Learning. What can you tell us about it, Patrick? Patrick: Oracle Machine Learning, or OML, is available with Autonomous Database. A new capability that we've introduced with Oracle Machine Learning is called Automatic Machine Learning, or AutoML. Its goal is to increase data scientist productivity while reducing overall compute time. In addition, AutoML enables non-experts to leverage machine learning by not requiring deep understanding of the algorithms and their settings.

05:37

Lois: And what are the key functions of AutoML? Patrick: AutoML consists of three main functions: Algorithm Selection, Feature Selection, and Model Tuning. With Automatic Algorithm Selection, the goal is to identify the in-database algorithms that are likely to achieve the highest model quality. Using metalearning, AutoML leverages machine learning itself to help find the best algorithm faster than with exhaustive search. With Automatic Feature Selection, the goal is to denoise data by eliminating features that don't add value to the model. By identifying the most predicted features and eliminating noise, model accuracy can often be significantly improved with a side benefit of faster model building and scoring.

Automatic Model Tuning tunes algorithm hyperparameters, those parameters that determine the behavior of the algorithm, on the provided data. Auto Model Tuning can significantly improve model accuracy while avoiding manual or exhaustive search techniques, which can be costly both in terms of time and compute resources.

06:44

Lois: How does Oracle Machine Learning leverage the capabilities of Autonomous Database?

Patrick: With Oracle Machine Learning, the full power of the database is accessible with the tremendous performance of parallel processing available, whether the machine learning algorithm is accessed via native database SQL or with OML4Py through Python or R. 

07:07

Nikita: Patrick, talk to us about the Data Insights feature. How does it help analysts uncover hidden patterns and anomalies? Patrick: A feature I wanted to call the electromagnet, but they didn't let me. An analyst's job can often feel like looking for a needle in a haystack. So throw the switch and all that metallic stuff is going to slam up onto that electromagnet. Sure, there are going to be rusty old nails and screws and nuts and bolts, but there are going to be a few needles as well. It's far easier to pick the needles out of these few bits of metal than go rummaging around in a pile of hay, especially if you have allergies.

That's more or less how our Insights tool works. Load your data, kick off a query, and grab a cup of coffee. Autonomous Database does all the hard work, scouring through this data looking for hidden patterns, anomalies, and outliers. Essentially, we run some analytic queries that predict expected values. And where the actual values differ significantly from expectation, the tool presents them here. Some of these might be uninteresting or obvious, but some are worthy of further investigation. You get this dashboard of various exceptional data patterns. Drill down on a specific gauge in this dashboard and significant deviations between actual and expected values are highlighted.

08:28

Lois: What a useful feature! Thank you, Patrick. Now, let’s discuss some terms and concepts that are applicable to the Autonomous JSON Database with Kay. Hi Kay, what’s the main focus of the Autonomous JSON Database? How does it support developers in building NoSQL-style applications?

Kay: Autonomous Database supports the JavaScript Object Notation, also known as JSON, natively in the database. It supports applications that use the SODA API to store and retrieve JSON data or SQL queries to store and retrieve data stored in JSON-formatted data. 

Oracle AJD is Oracle ATP, Autonomous Transaction Processing, but it's designed for developing NoSQL-style applications that use JSON documents. You can promote an AJD service to ATP.

09:22

Nikita: What makes the development of NoSQL-style, document-centric applications flexible on AJD? 

Kay: Development of these NoSQL-style, document-centric applications is particularly flexible because the applications use schemaless data. This lets you quickly react to changing application requirements. There's no need to normalize the data into relational tables and no impediment to changing the data structure or organization at any time, in any way. A JSON document has its own internal structure, but no relation is imposed on separate JSON documents.

Nikita: What does AJD do for developers? How does it actually help them?

Kay: So Autonomous JSON Database, or AJD, is designed for you, the developer, to allow you to use simple document APIs and develop applications without having to know anything about SQL. That's a win.

But at the same time, it does give you the ability to create highly complex SQL-based queries for reporting and analysis purposes. It has built-in binary JSON storage type, which is extremely efficient for searching and for updating. It also provides advanced indexing capabilities on the actual JSON data.

It's built on Autonomous Database, so that gives you all of the self-driving capabilities we've been talking about, but you don't need a DBA to look after your database for you. You can do it all yourself.

11:00

Lois: For listeners who may not be familiar with JSON, can you tell us briefly what it is? 

Kay: So I mentioned this earlier, but it's worth mentioning again. JSON stands for JavaScript Object Notation. It was originally developed as a human readable way of providing information to interchange between different programs.

So a JSON document is a set of fields. Each of these fields has a value, and those values can be of various data types. We can have simple strings, we can have integers, we can even have real numbers. We can have Booleans that are true or false. We can have date strings, and we can even have the special value null.

Additionally, values can be objects, and objects are effectively whole JSON documents embedded inside a document. And of course, there's no limit on the nesting. You can nest as far as you like. Finally, we can have a raise, and a raise can have a list of scalar data types or a list of objects.

12:13

Nikita: Kay, how does the concept of schema apply to JSON databases?

Kay: Now, JSON documents are stored in something that we call collections. Each document may have its own schema, its own layout, to the JSON. So does this mean that JSON document databases are schemaless? Hmmm. Well, yes. But there's nothing to fear because you can always use a check constraint to enforce a schema constraint that you wish to introduce to your JSON data.

Lois: Kay, what about indexing capabilities on JSON collections?

Kay: You can create indexes on a JSON collection, and those indexes can be of various types, including our flexible search index, which indexes the entire content of the document within the JSON collection, without having to know anything in advance about the schema of those documents. 

Lois: Thanks Kay!

13:18

AI is being used in nearly every industry—healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And, it’s only going to get more prevalent and transformational in the future. So it’s no wonder that AI skills are the most sought after by employers. 

We’re happy to announce a new OCI AI Foundations certification and course that is available—for FREE! Want to learn about AI? Then this is the best place to start! So, get going! Head over to mylearn.oracle.com to find out more. 

13:54

Nikita: Welcome back! Sangeetha, I want to bring you in to talk about Oracle Text. Now I know that Oracle Database is not only a relational store but also a document store. And you can load text and JSON assets along with your relational assets in a single database. 

When I think about Oracle and databases, SQL development is what immediately comes to mind. So, can you talk a bit about the power of SQL as well as its challenges, especially in schema changes?

Sangeetha: Traditionally, Oracle has been all about SQL development. And with SQL development, it's an incredibly powerful language. But it does take some advanced knowledge to make the best of it.

So SQL requires you to define your schema up front. And making changes to that schema could be a little tricky and sometimes highly bureaucratic task. In contrast, JSON allows you to develop your schema as you go--the schemaless, perhaps schema-later model. By imposing less rigid requirements on the developer, it allows you to be more fluid and Agile development style.

15:09

Lois: How does Oracle Text use SQL to index, search, and analyze text and documents that are stored in the Oracle Database?

Sangeetha: Oracle Text can perform linguistic analyses on documents as well as search text using a variety of strategies, including keyword searching, context queries, Boolean operations, pattern matching, mixed thematic queries, like HTML/XML session searching, and so on.

It can also render search results in various formats, including unformatted text, HTML with term highlighting, and original document format. Oracle Text supports multiple languages and uses advanced relevance-ranking technology to improve search quality. Oracle Text also offers advantage features like classification, clustering, and support for information visualization metaphors.

Oracle Text is now enabled automatically in Autonomous Database. It provides full-text search capabilities over text, XML, JSON content. It also could extend current applications to make better use of textual fields. It builds new applications specifically targeted at document searching.

Now, all of the power of Oracle Database and a familiar development environment, rock-solid autonomous database infrastructure for your text apps, we can deal with text in many different places and many different types of text. So it is not just in the database. We can deal with data that's outside of the database as well.

17:03

Nikita: How does it handle text in various places and formats, both inside and outside the database?

Sangeetha: So in the database, we can be looking a varchar2 column or LOB column or binary LOB columns if we are talking about binary documents such as PDF or Word. Outside of the database, we might have a document on the file system or out on the web with URLs pointing out to the document.

If they are on the file system, then we would have a file name stored in the database table. And if they are on the web, then we should have a URL or a partial URL stored in the database. And we can then fetch the data from the locations and index it in the term documents format.

We recognize many different document formats and extract the text from them automatically. So the basic forms we can deal with-- plain text, HTML, JSON, XML, and then formatted documents like Word docs, PDF documents, PowerPoint documents, and also so many different types of documents. All of those are automatically handled by the system and then processed into the format indexing.

And we are not restricted by the English either here. There are various stages in the index pipeline. A document starts one, and it's taken through the different stages so until it finally reaches the index.

18:44

Lois: You mentioned the indexing pipeline. Can you take us through it?

Sangeetha: So it starts with a data store. That's responsible for actually reaching the document. So once we fetch the document from the data store, we pass it on to the filter. And now the filter is responsible for processing binary documents into indexable text.

So if you have a PDF, let's say a PDF document, that will go through the filter. And that will extract any images and return it into the stream of HTML text ready for indexing. Then we pass it on to the sectioner, which is responsible for identifying things like paragraphs and sentences. The output from the section is fed onto the lexer.

The lexer is responsible for dividing the text into indexable words. The output of the lexer is fed into the index engine, which is responsible for laying out to the indexes on the disk. Storage, word list, and stop list are some additional inputs there. So storage tells exactly how to lay out the index on disk. Word list which has special preferences like desegmentation. And then stop is a list word that we don't want to index. So each of these stages and inputs can be customized.

Oracle has something known as the extensibility framework, which originally was designed to allow people to extend capabilities of these products by adding new domain indexes. And this is what we've used to implement Oracle Text. So when kernel sees this phrase INDEXTYPE ctxsys.context, it knows to handle all of the hard work creating the index.

20:48

Nikita: Other than text indexing, Oracle Text offers additional operations, right? Can you share some examples of these operations?

Sangeetha: So beyond the text index, other operations that we can do with the Oracle Text, some of which are search related. And some examples of that are these highlighting markups and snippets. Highlighting and markup are very similar. They are ways of fetching these results back with the search. And then it's marked up with highlighting within the document text. Snippet is very similar, but it's only bringing back the relevant chunks from the document that we are searching for. So rather than getting the whole document back to you, just get a few lines showing this in a context and the theme and extraction. So Oracle Text is capable of figuring out what a text is all about. We have a very large knowledge base of the English language, which will allow you to understand the concepts and the themes in the document.

Then there's entity extraction, which is the ability to find out people, places, dates, times, zip codes, et cetera in the text. So this can be customized with your own user dictionary and your own user rules.

22:14

Lois: Moving on to advanced functionalities, how does Oracle Text utilize machine learning algorithms for document classification? And what are the key types of classifications? Sangeetha: The text analytics uses machine learning algorithms for document classification. We can process a large set of data documents in a very efficient manner using Oracle's own machine learning algorithms. So you can look at that as basically three different headings. First of all, there's classification. And that comes in two different types-- supervised and unsupervised.

The supervised classification which means in this classification that it provides the training set, a set of documents that have already defined particular characteristics that you're looking for. And then there's unsupervised classification, which allows your system itself to figure out which documents are similar to each other.

It does that by looking at features within the documents. And each of those features are represented as a dimension in a massively high dimensional feature space in documents, which are clustered together according to that nearest and nearness in the dimension in the feature space.

Again, with the named entity recognition, we've already talked about that a little bit. And then finally, there is a sentiment analysis, the ability to identify whether the document is positive or negative within a given particular aspect.

23:56

Nikita: Now, for those who are already Oracle database users, how easy is it to enable text searching within applications using Oracle Text?

Sangeetha: If you're already an Oracle database user, enabling text searching within your applications is quite straightforward. Oracle Text uses the same SQL language as the database. And it integrates seamlessly with your existing SQL. Oracle Text can be used from any programming language which has SQL interface, meaning just about all of them. 

24:32

Lois: OK from Oracle Text, I’d like to move on to Oracle Spatial Studio. Can you tell us more about this tool?

Sangeetha: Spatial Studio is a no-code, self-service application that makes it easy to access the sorts of spatial features that we've been looking at, in particular, in order to get that data prepared to use with spatial, visualizing results in maps and tables, and also doing the analysis and sharing results. Spatial Studios is encoded at no extra cost with Autonomous Database. The studio web application itself has no additional cost and it runs on the server.

25:13

Nikita: Let’s talk a little more about the cost. How does the deployment of Spatial Studio work, in terms of the server it runs on? 

Sangeetha: So, the server that it runs on, if it's running in the Cloud, that computing node, it would have some cost associated with it. It can also run on a free tier with a very small shape, just for evaluation and testing. 

Spatial Studio is also available on the Oracle Cloud Marketplace. And there are a couple of self-paced workshops that you can access for installing and using Spatial Studio.

25:47

Lois: And how do developers access and work with Oracle Autonomous Database using Spatial Studio?

Sangeetha: Oracle Spatial Studio allows you to access data in Oracle Database, including Oracle Autonomous Database. You can create connections to Oracle Autonomous Databases, and then you work with the data that's in the database. You can also see Spatial Studio to load data to Oracle Database, including Oracle Autonomous Database.

So, you can load these spreadsheets in common spatial formats. And once you've loaded your data or accessed data that already exists in your Autonomous Database, if that data does not already include native geometrics, Oracle native geometric type, then you can prepare the data if it has addresses or if it has latitude and longitude coordinates as a part of the data.

26:43

Nikita: What about visualizing and analyzing spatial data using Spatial Studio?

Sangeetha: Once you have the data prepared, you can easily drag and drop and start to visualize your data, style it, and look at it in different ways. And then, most importantly, you can start to ask spatial questions, do all kinds of spatial analysis, like we've talked about earlier.

While Spatial Studio provides a GUI that allows you to perform those same kinds of spatial analysis. And then the results can be dropped on the map and visualized so that you can actually see the results of spatial questions that you're asking. When you've done some work, you can save your work in a project that you can return to later, and you can also publish and share the work you've done.

27:34

Lois: Thank you, Sangeetha. For the final part of our conversation today, we’ll talk with Thea. Thea, thanks so much for joining us. Let's get the basics out of the way. How can data be loaded directly into Autonomous Database? Thea: Data can be loaded directly to ADB through applications such as SQL Developer, which can read data files, such as txt and xls, and load directly into tables in ADB.

27:59

Nikita: I see. And is there a better method to load data into ADB? Thea: A more efficient and preferred method for loading data into ADB is to stage the data cloud object store, preferably Oracle's, but also supported our Amazon S3 and Azure Blob Storage. Any file type can be staged in object store. Once the data is in object store, Autonomous Database can access a directly. Tools can be used to facilitate the data movement between object store and the database.

28:27

Lois: Are there specific steps or considerations when migrating a physical database to Autonomous?

Thea: A physical database can simply be migrated to autonomous because database must be converted to pluggable database, upgraded to 19C, and encrypted. Additionally, any changes to an Oracle-shipped stored procedures or views must be found and reverted. All uses of container database admin privileges must be removed. And all legacy features that are not supported must be removed, such as legacy LOBs. Data Pump, expdp/impdp must be used for migrating databases versions 10.1 and above to Autonomous Database as it addresses the issues just mentioned. For online migrations, GoldenGate must be used to keep old and new database in sync.

29:15

Nikita: When you’re choosing the method for migration and loading, what are the factors to keep in mind?

Thea: It's important to segregate the methods by functionality and limitations of use against Autonomous Database. The considerations are as follows. Number one, how large is the database to be imported? Number two, what is the input file format? Number three, does the method support non-Oracle database sources? And number four, does the methods support using Oracle and/or third-party object store?

29:45

Lois: Now, let’s move on to the tools that are available. What does the DBMS_CLOUD functionality do?

Thea: The Oracle Autonomous Database has built-in functionality called DBMS_CLOUD specifically designed so the database can move data back and forth with external sources through a secure and transparent process. DBMS_CLOUD allows data movement from the Oracle object store. Data from any application or data source export to text-- .csv or JSON-- output from third-party data integration tools.

DBMS_CLOUD can also access data stored on Object Storage from the other clouds, AWS S3 and Azure Blob Storage. DBMS_CLOUD does not impose any volume limit, so it's the preferred method to use. SQL*Loader can be used for loading data located on the local client file systems into Autonomous Database. There are limits around OS and client machines when using SQL*Loader.

30:49

Nikita: So then, when should I use Data Pump and SQL Developer for migration?

Thea: Data Pump is the best way to migrate a full or part database into ADB, including databases from previous versions. Because Data Pump will perform the upgrade as part of the export/import process, this is the simplest way to get to ADB from any existing Oracle Database implementation. SQL Developer provides a GUI front end for using data pumps that can automate the whole export and import process from an existing database to ADB.

SQL Developer also includes an import wizard that can be used to import data from several file types into ADB. A very common use of this wizard is for importing Excel files into ADW. Once a credential is created, it can be used to access a file as an external table or to ingest data from the file into a database table. DBMS_CLOUD makes it much easier to use external tables, and the organization external needed in other versions of the Oracle Database are not needed.

31:54

Lois: Thea, what about Oracle Object Store? How does it integrate with Autonomous Database, and what advantages does it offer for staging data?

Thea: Oracle Object Store is directly integrated into Autonomous Database and is the best option for staging data that will be consumed by ADB. Any file type can be stored in object store, including SQL*Loader files, Excel, JSON, Parquet, and, of course, Data Pump DMP files. Flat files stored on object store can also be used as Oracle Database external tables, so they can queried directly from the database as part of a normal DML operation.

Object store is a separate bin storage allocated to the Autonomous Database for database Object Storage, such as tables and indexes. That storage is part of the Exadata system Autonomous Database runs on, and it is automatically allocated and managed. Users do not have direct access to that storage.

32:50

Nikita: I know that one of the main considerations when loading and updating ADB is the network latency between the data source and the ADB. Can you tell us more about this?

Thea: Many ways to measure this latency exist. One is the website cloudharmony.com, which provides many real-time metrics for connectivity between the client and Oracle Cloud Services. It's important to run these tests when determining with Oracle Cloud service location will provide the best connectivity.

The Oracle Cloud Dashboard has an integrated tool that will provide real time and historic latency information between your existing location and any specified Oracle Data Center. When migrating data to Autonomous Database, table statistics are gathered automatically during direct-path load operations. If direct-path load operations are not used, such as with SQL Developer loads, the user can gather statistics manually as needed.

33:44

Lois: And finally, what can you tell us about the Data Migration Service?

Thea: Database Migration Service is a fully managed service for migrating databases to ADB. It provides logical online and offline migration with minimal downtime and validates the environment before migration. We have a requirement that the source database is on Linux. And it would be interesting to see if we are going to have other use cases that we need other non-Linux operating systems.

This requirement is because we are using SSH to directly execute commands on the source database. For this, we are certified on the Linux only. Target in the first release are Autonomous databases, ATP, or ADW, both serverless and dedicated. For agent environment, we require Linux operating system, and this is Linux-safe. In general, we're targeting a number of different use cases-- migrating from on-premise, third-party clouds, Oracle legacy clouds, such as Oracle Classic, or even migrating within OCI Cloud and doing that with or without direct connection.

If you have any direct connection behind a firewall, we support offline migration. If you have a direct connection, we support both offline and online migration. For more information on all migration approaches are available for your particular situation, check out the Oracle Cloud Migration Advisor.

35:06

Nikita: I think we can wind up our episode with that. Thanks to all our experts for giving us their insights. 

Lois: To learn more about the topics we’ve discussed today, visit mylearn.oracle.com and search for the Oracle Autonomous Database Administration Workshop. Remember, all of the training is free, so dive right in! Join us next week for another episode of the Oracle University Podcast. Until then, Lois Houston…

Nikita: And Nikita Abraham, signing off!

35:35

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

The Oracle Autonomous Database Dedicated deployment is a good choice for customers who want to implement a private database cloud in their own dedicated Exadata infrastructure. That dedicated infrastructure can either be in the Oracle Public Cloud or in the customer's own data center via Oracle Exadata Cloud@Customer.   In a dedicated environment, the Exadata infrastructure is entirely dedicated to the subscribing customer, isolated from other cloud tenants, with no shared processor, storage, and memory resource.   In this episode, hosts Lois Houston and Nikita Abraham speak with Oracle Database experts about how Autonomous Database Dedicated offers greater control of the software and infrastructure life cycle, customizable policies for separation of database workload, software update schedules and versioning, workload consolidation, availability policies, and much more.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Tamal Chatterjee, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------   Episode Transcript:  

00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! This is our second episode on Oracle’s Autonomous Database, and today we’re going to spend time discussing Autonomous Database on Dedicated Infrastructure. We’ll be talking with three of our colleagues: Maria Colgan, Kamryn Vinson, and Kay Malcolm. 00:53 Nikita: Maria is a Distinguished Product Manager for Oracle Database, Kamryn is a Database Product Manager, and Kay is a Senior Director of Database Product Management.  Lois: Hi Maria! Thanks for joining us today. We know that Oracle Autonomous Database offers two deployment choices: serverless and dedicated Exadata infrastructure. We spoke about serverless infrastructure last week but for anyone who missed that episode, can you give us a quick recap of what it is? 01:22 Maria: With Autonomous Database Serverless, Oracle automates all aspects of the infrastructure and database management for you. That includes provisioning, configuring, monitoring, backing up, and tuning. You simply select what type of database you want, maybe a data warehouse, transaction processing, or a JSON document store, which region in the Oracle Public Cloud you want that database deployed, and the base compute and storage resources necessary. Oracle automatically takes care of everything else. Once provisioned, the database can be instantly scaled through our UI, our APIs, or automatically based on your workload needs. All scaling activities happen completely online while the database remains open for business. 02:11 Nikita: Ok, so now that we know what serverless is, let’s move on to dedicated infrastructure. What can you tell us about it? Maria: Autonomous Database Dedicated allows customers to implement a private database cloud running on their own dedicated Exadata infrastructure. That dedicated infrastructure can be in Oracle’s Public Cloud or in the customer's own data center via Oracle Exadata Cloud@Customer. It makes an ideal platform to consolidate multiple databases regardless of their workload type or their size. And it also allows you to offer database as a service within your enterprise. 02:50 Lois: What are the primary benefits of Autonomous Database Dedicated infrastructure? Maria: With the dedicated deployment option, you must first subscribe to Dedicated Exadata Cloud Infrastructure that is isolated from other tenants with no shared processors, memory, network, or storage resources. This infrastructure choice offers greater control of both the software and the infrastructure life cycle. Customers can specify their own policies for workload separation, software update schedules, and availability. One of the key benefits of an autonomous database is a lower total cost of ownership through more automation and operational delegation to Oracle. Remember it’s a fully managed service. All database operations, such as backup, software updates, upgrades, OS maintenance, incident management, and health monitoring, will be automatically done for you by Oracle. Its maximum availability architecture protects you from any hardware failures and in the event of a full outage, the service will be automatically failed over to your standby site. Built-in application continuity ensures zero downtime during the standard software update or in the event of a failover.  04:09 Nikita: And how is this billed?  Maria: Autonomous Database also has true pay-per-use billing so even when autoscale is enabled, you’ll only pay for those additional resources when you use them. And we make it incredibly simple to develop on this environment with managed developer add-ons like our low code development environment, APEX, and our REST data services. This means you don’t need any additional development environments in order to get started with a new application. 04:40 Lois: Ok. So, it looks like the dedicated option offers more control and customization. Maria, how do we access a dedicated database over a network? Maria: The network path is through a VCN, or Virtual Cloud Network, and the subnet that's defined by the Exadata infrastructure hosting the database. By default, this subnet is defined as private, meaning, there's no public internet access to those databases. This ensures only your company can access your Exadata infrastructure and your databases. Autonomous Database Dedicated can also take advantage of network services provided by OCI, including subnets or VCN peering, as well as connections to on-prem databases through the IP secure VPN and FastConnect dedicated corporate network connections. 05:33 Maria: You can also take advantage of the Oracle Microsoft partnership that enables customers to connect their Oracle Cloud Infrastructure resources and Microsoft Azure resources through a dedicated private connection. However, for some customers, a move to the public cloud is just not possible. Perhaps it's due to industry regulations, performance concerns, or integration with legacy on-prem applications. For these types of customers, Exadata Cloud@Customer should meet their requirements for strict data sovereignty and security by delivering high-performance Exadata Cloud Services capabilities in their data center behind their own firewall. 06:16 Nikita: What are the benefits of Autonomous Database on Exadata Cloud@Customer? How’s it different? Maria: Autonomous Database on Exadata Cloud@Customer provides the same service as Autonomous Database Dedicated in the public cloud. So you get the same simplicity, agility, and performance, and elasticity that you get in the cloud. But it also provides a very fast and simple transition to an autonomous cloud because you can easily migrate on-prem databases to Exadata Cloud@Customer. Once the database is migrated, any existing applications can simply reconnect to that new database and run without any application changes being needed. And the data will leave your data center, so making it a very safe way to adopt a cloud model. 07:04 Lois: So, how do we manage communication to and from the public cloud? Maria: Each Cloud@Customer rack includes two local control plane servers to manage the communication to and from the public cloud. The local control plane acts on behalf of requests from the public cloud, keeping communications consolidated and secure. Platform control plane commands are sent to the Exadata Cloud@Customer system through a dedicated WebSocket secure tunnel.  Oracle Cloud operations staff use that same tunnel to monitor the autonomous database on Exadata Cloud@Customer both for maintenance and for troubleshooting. The two remote, control plane servers installed in the Exadata Cloud@Customer rack host that secure tunnel endpoint and act as a gateway for access to the infrastructure. They also host components that orchestrate the cloud automation, aggregates and routes telemetry messages from the Exadata Cloud@Customer platform to the Oracle Support Service infrastructure. And they also host images for server patching. 08:13 Maria: The Exadata Database Server is connected to the customer-managed switches via either 10 gigabit or 25 gigabit Ethernet. Customers have access to the customer Virtual Machine, or VM, via a pair of layer 2 network connections that are implemented as Virtual Network Interface Cards, or vNICs. They're also tagged VLAN. The physical network connections are implemented for high availability in an active standby configuration. Autonomous Database on Exadata Cloud@Customer provides the best of both worlds-- all of the automation including patching, backing up, scaling, and management of a database that you get with a cloud service, but without the data ever leaving the customer's data center. 09:01 Nikita: That's interesting. And, what happens if a dedicated database loses network connectivity to the OCI control plane? Maria: In the event an autonomous database on Exadata Cloud@Customer loses network connectivity to the OCI control plane, the Autonomous Database will actually continue to be available for your applications. And operations such as backups and autoscaling will not be impacted in that loss of network connectivity. However, the management and monitoring of the Autonomous Database via the OCI console and APIs as well as access by the Oracle Cloud operations team will not be available until that network is reconnected. 09:43 Maria: The capability suspended in the case of a lost network connection include, as I said, infrastructure management-- so that's the manual scaling of an Autonomous Database via the UI or our OCI CLI, or REST APIs, as well as Terraform scripts. They won't be available. Neither will the ability for Oracle Cloud ops to access and perform maintenance activities, such as patching. Nor will we be able to monitor the Oracle infrastructure during the time where the system is not connected. 10:20 Lois: That’s good to know, Maria. What about data encryption and backup options? Maria: All Oracle Autonomous Databases encrypt data at REST. Data is automatically encrypted as it's written to the storage. But this encryption is transparent to authorized users and applications because the database automatically decrypts the data when it's being read from the storage. There are several options for backing up the Autonomous Database Cloud@Customer including using a Zero Data Loss Recovery Appliance, or ZDLRA. You can back it up to locally mounted NFS storage or back it up to the Oracle Public Cloud. 10:57 Nikita: I want to ask you about the typical workflow for Autonomous Database Dedicated infrastructure. What are the main steps here? Maria: In the typical workflow, the fleet administrator role performs the following steps. They provision the Exadata infrastructure by specifying its size, availability domain, and region within the Oracle Cloud. Once the hardware has been provisioned, the fleet administrator partitions the system by provisioning clusters and container databases. Then the developers, DBAs, or anyone who needs a database can provision databases within those container databases. Billing is based on the size of the Exadata infrastructure that's provisioned. So whether that's a quarter rack, half rack, or full rack. It also depends on the number of CPUs that are being consumed. Remember, it's also possible for customers to use their existing Oracle database licenses with this service to reduce the cost. 11:53 Lois: And what Exadata infrastructure models and shapes does Autonomous Database Dedicated support? Maria: That's the X7, X8, and X8M and you can get all of those in either a quarter, half, or full Exadata rack. Currently, you can create a maximum of 12 VM clusters on an Autonomous Database Dedicated infrastructure. We also advise that you limit the number of databases you provision to meet your preferred SLA. To meet the high availability SLA, we recommend a maximum of 100 databases. To meet the extreme availability SLA, we recommend a maximum of 25 databases. 12:35 Nikita: Ok, so now that I know all this, how do I actually get started with Autonomous Database on dedicated infrastructure? Maria: You need to increase your service limit to include that Exadata infrastructure and then you need to create the fleet and DBA service roles. You also need to create the necessary network model, VM clusters, and container databases for your organization. Finally, you need to provide access to the end users who want to create and use those Autonomous databases. Autonomous Database requires a subscription to that Exadata infrastructure for a minimum of 48 hours. But once subscribed, you can test out ideas and then terminate the subscription with no ongoing costs. While subscribed, you can control where you place the resources to perhaps manage latency sensitive applications. 13:29 Maria: You can also have control over patching schedules, software versions, so you can be sure that you're testing exactly what you need to. You can also migrate databases to the Autonomous Database via our export, import capabilities via the object store or through Data Pump or Golden Gate. As with any Autonomous Database, once it's provisioned, you've got full access to both autoscaling and all our cloning capabilities.  13:57 Lois: Maria, I've heard you talk about the importance of clean role separation in managing a private cloud. Can you elaborate on that, please? Maria: A successful private cloud is set up and managed using clean role separation between the fleet administration group and the developers, or DBA groups. The fleet administration group establishes the governance constraints, including things like budgeting, capacity compliance, and SLAs, according to the business structure. The physical resources are also logically grouped to align with this business structure, and then groups of users are given self-service access to the resources within these groups. So a good example of this would be that the developers and DBA groups use self-service database resources within these constraints. 14:46 Nikita: I see. So, what exactly does a fleet administrator do? Maria: Fleet administrators allocate budget by department and are responsible for the creation, monitoring, and management of the autonomous exadata infrastructure, the autonomous exadata VM clusters, and the autonomous container databases. To perform these duties, the fleet administrators must have an Oracle Cloud account or user, and that user must have permissions to manage these resources and be permitted to use network resources that need to be specified when you create these other resources. 15:24 Nikita: And what about database administrators?

Maria: Database administrators create, monitor, and manage autonomous databases. They, too, need to have an Oracle Cloud account or be an Oracle Cloud user. Now, those accounts need to have the necessary permissions in order to create and access databases. They also need to be able to access autonomous backups and have permission to access the autonomous container databases, inside which these autonomous databases will be created, and have all of the necessary permissions to be able to create those databases, as I said. While creating autonomous databases, the database administrators will define and gain access to an admin user account inside the database. It's through this account that they will actually get the necessary permissions to be able to create and control database users.  16:24 Lois: How do developers fit into the picture? Maria: Database users and developers who write applications that will use or access an autonomous database don't actually need Oracle Cloud accounts. They'll actually be given the network connectivity and authorization information they need to access those databases by the database administrators. 16:45 Lois: Maria, you mentioned the various ways to manage the lifecycle of an autonomous dedicated service. Can you tell us more about that? Maria: You can manage the lifecycle of an autonomous dedicated service through the Cloud UI, Command Line Interface, through our REST APIs, or through one of the several language SDKs. The lifecycle operations that you can manage include capacity planning and setup, the provisioning and partitioning of exadata infrastructure, the provisioning and management of databases, the scaling of CPU storage and other resources, the scheduling of updates for the infrastructure, the VMs, and the database, as well as monitoring through event notifications.  17:30 Lois: And how do policies come into play? Maria: OCI allows fine-grained control over resources through the application of policies to groups. These policies are applicable to any member of the group. For Oracle Autonomous Database on dedicated infrastructure, the resources in question are autonomous exadata infrastructure, autonomous container databases, autonomous databases, and autonomous backups.  Lois: Thanks so much, Maria. That was great information. 18:05 The Oracle University Learning Community is a great place for you to collaborate and learn with experts, peers, and practitioners. Grow your skills, inspire innovation, and celebrate your successes. The more you participate, the more recognition you can earn. All of your activities, from liking a post to answering questions and sharing with others, will help you earn badges and ranks, and be recognized within the community.

If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 18:44 Nikita: Welcome back! Hi Kamryn, thanks for joining us on the podcast. So, in an Autonomous Database environment where most DBA tasks are automated, what exactly does an application DBA do? Kamryn: While Autonomous Database automates most of the repetitive tasks that DBAs perform, the application DBA will still want to monitor and diagnose databases for applications to maintain the highest performance and the greatest security possible. Tasks the application DBA performs includes operations on databases, cloning, movement, monitoring, and creating alerts. When required, the application DBA performs low-level diagnostics for application performance and looks for insights on performance and capacity trends.  19:36 Nikita: I see. And which tools do they use for these tasks? Kamryn: There are several tools at the application DBA's disposal, including Enterprise Manager, Performance Hub, and the OCI Console. For Autonomous Dedicated, all the database operations are exposed through the console UI and available through REST API calls, including provisioning, stop/start, lifecycle operations for dedicated database types, unscheduled on-demand backups and restores, CPU scaling and storage management, providing connectivity information, including wallets, scheduling updates. 20:17 Lois: So, Kamryn, what tools can DBAs use for deeper exploration? Kamryn: For deeper exploration of the databases themselves, Autonomous Database DBAs can use SQL Developer Web, Performance Hub, and Enterprise Manager. 20:31 Nikita: Let’s bring Kay into the conversation. Hi Kay! With Autonomous Database Dedicated, I’ve heard that customers have more control over patching. Can you tell us a little more about that? Kay: With Autonomous Database Dedicated, customers get to determine the update or patching schedule if they wish. Oracle automatically manages all patching activity, but with the ADB-Dedicated service, customers have the option of customizing the patching schedule. You can specify which month in every quarter you want, which week in that month, which day in that month, and which patching window within that day. You can also dynamically change the scheduled patching date and time for a specific database if the originally scheduled time becomes inconvenient.

21:22 Lois: That's great! So, how often are updates published, and what options do customers have when it comes to applying these updates? Kay: Every quarter, updates are published to the console, and OCI notifications are sent out. ADB-Dedicated allows for greater control over updates by allowing you to choose to apply the current update or stay with the previous version and skip to the next release. And the latest update can be applied immediately. This provides fleet administrators with the option to maintain test and production systems at different patch levels. A fleet administrator or a database admin sets up the software version policy at the Autonomous Container Database level during provisioning, although the defaults can be modified at any time for an existing Autonomous Container Database. At the bottom of the Autonomous Exadata Infrastructure provisioning screen, you will see a Configure the Automatic Maintenance section, where you should click the Modify Schedule.  22:34 Nikita: What happens if a customer doesn't customize their patching schedule? Kay: If you do not customize a schedule, it behaves like Autonomous Serverless, and Oracle will set a schedule for you. ADB-Dedicated customers get to choose the patching schedule that fits their business.  22:52 Lois: Back to you, Kamryn, I know a bit about Transparent Data Encryption, but I'm curious to learn more. Can you tell me what it does and how it helps protect data? Kamryn: Transparent Data Encryption, TDE, enables you to encrypt sensitive data that you store in tables and tablespaces. After the data is encrypted, this data is transparently decrypted for authorized users or applications when they access this data. TDE helps protect data stored on media, also called data at rest. If the storage media or data file is stolen, Oracle database uses authentication, authorization, and auditing mechanisms to secure data in the database, but not in the operating system data files where data is stored. To protect these data files, Oracle database provides TDE.  23:45 Nikita: That sounds important for data security. So, how does TDE protect data files? Kamryn: TDE encrypts sensitive data stored in data files. To prevent unauthorized decryption, TDE stores the encryption keys in a security module external to the database called a keystore. You can configure Oracle Key Vault as part of the TDE implementation. This enables you to centrally manage TDE key stores, called TDE wallets, in Oracle Key Vault in your enterprise. For example, you can upload a software keystore to Oracle Key Vault and then make the contents of this keystore available to other TDE-enabled databases. 24:28 Lois: What about Oracle Autonomous Database? How does it handle encryption? Kamryn: Oracle Autonomous Database uses always-on encryption that protects data at rest and in transit. All data stored in Oracle Cloud and network communication with Oracle Cloud is encrypted by default. Encryption cannot be turned off. By default, Oracle Autonomous Database creates and manages all the master encryption keys used to protect your data, storing them in a secure PKCS 12 keystore on the same Exadata systems where the databases reside. If your company's security policies require, Oracle Autonomous Database can instead use keys you create and manage. Customers can control key generation and rotation of the keys. 25:19 Kamryn: The Autonomous databases you create automatically use customer-managed keys because the Autonomous container database in which they are created is configured to use customer-managed keys. Thus, those users who create and manage Autonomous databases do not have to worry about configuring their databases to use customer-managed keys. 25:41 Nikita: Thank you so much, Kamryn, Kay, and Maria for taking the time to give us your insights. To learn more about provisioning Autonomous Database Dedicated resources, head over to mylearn.oracle.com and search for the Oracle Autonomous Database Administration Workshop. Lois: In our next episode, we will discuss Autonomous Database tools. Until then, this is Lois Houston… Nikita: …and Nikita Abraham signing off. 26:07 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Want to quickly provision your autonomous database? Then look no further than Oracle Autonomous Database Serverless, one of the two deployment choices offered by Oracle Autonomous Database.   Autonomous Database Serverless delegates all operational decisions to Oracle, providing you with a completely autonomous experience.   Join hosts Lois Houston and Nikita Abraham, along with Oracle Database experts, as they discuss how serverless infrastructure eliminates the need to configure any hardware or install any software because Autonomous Database handles provisioning the database, backing it up, patching and upgrading it, and growing or shrinking it for you.   Oracle Autonomous Database Episode: https://oracleuniversitypodcast.libsyn.com/oracle-autonomous-database Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Rajeev Grover, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:  

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started.

00:26

Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor.

Nikita: Hi everyone! Welcome back to a new season of the Oracle University Podcast. This time, our focus is going to be on Oracle Autonomous Database. We’ve got a jam-packed season planned with some very special guests joining us.

00:52

Lois: If you’re a regular listener of the podcast, you’ll remember that we’d spoken a bit about Autonomous Database last year. That was a really good introductory episode so if you missed it, you might want to check it out. 

Nikita: Yeah, we’ll post a link to the episode in today’s show notes so you can find it easily.

01:07

Lois: Right, Niki. So, for today’s episode, we wanted to focus on Autonomous Database on Serverless Infrastructure and we reached out to three experts in the field: Hannah Nguyen,  Sean Stacey, and Kay Malcolm. Hannah is an Associate Cloud Engineer, Sean, a Director of Platform Technology Solutions, and Kay, who’s been on the podcast before, is Senior Director of Database Product Management. For this episode, we’ll be sharing portions of our conversations with them. So, let’s get started.

01:38

Nikita: Hi Hannah! How does Oracle Cloud handle the process of provisioning an Autonomous Database?  

Hannah: The Oracle Cloud automates the process of provisioning an Autonomous Database, and it automatically provisions for you a highly scalable, highly secure, and a highly available database very simply out of the box.

01:56

Lois: Hannah, what are the components and architecture involved when provisioning an Autonomous Database in Oracle Cloud?

Hannah: Provisioning the database involves very few steps. But it's important to understand the components that are part of the provisioned environment. When provisioning a database, the number of CPUs in increments of 1 for serverless, storage in increments of 1 terabyte, and backup are automatically provisioned and enabled in the database. In the background, an Oracle 19c pluggable database is being added to the container database that manages all the user's Autonomous Databases. Because this Autonomous Database runs on Exadata systems, Real Application Clusters is also provisioned in the background to support the on-demand CPU scalability of the service. This is transparent to the user and administrator of the service. But be aware it is there.

02:49

Nikita: Ok…So, what sort of flexibility does the Autonomous Database provide when it comes to managing resource usage and costs, you know… especially in terms of starting, stopping, and scaling instances?

Hannah: The Autonomous Database allows you to start your instance very rapidly on demand. It also allows you to stop your instance on demand as well to conserve resources and to pause billing. Do be aware that when you do pause billing, you will not be charged for any CPU cycles because your instance will be stopped. However, you'll still be incurring charges for your monthly billing for your storage. In addition to allowing you to start and stop your instance on demand, it's also possible to scale your database instance on demand as well. All of this can be done very easily using the Database Cloud Console.

03:36

Lois: What about scaling in the Autonomous Database?

Hannah: So you can scale up your OCPUs without touching your storage and scale it back down, and you can do the same with your storage. In addition to that, you can also set up autoscaling. So the database, whenever it detects the need, will automatically scale up to three times the base level number of OCPUs that you have allocated or provisioned for the Autonomous Database.

04:00

Nikita: Is autoscaling available for all tiers? 

Hannah: Autoscaling is not available for an always free database, but it is enabled by default for other tiered environments. Changing the setting does not require downtime. So this can also be set dynamically. One of the advantages of autoscaling is cost because you're billed based on the average number of OCPUs consumed during an hour.

04:23

Lois: Thanks, Hannah! Now, let’s bring Sean into the conversation. Hey Sean, I want to talk about moving an autonomous database resource. When or why would I need to move an autonomous database resource from one compartment to another?

Sean: There may be a business requirement where you need to move an autonomous database resource, serverless resource, from one compartment to another. Perhaps, there's a different subnet that you would like to move that autonomous database to, or perhaps there's some business applications that are within or accessible or available in that other compartment that you wish to move your autonomous database to take advantage of.

04:58

Nikita: And how simple is this process of moving an autonomous database from one compartment to another? What happens to the backups during this transition?

Sean: The way you can do this is simply to take an autonomous database and move it from compartment A to compartment B. And when you do so, the backups, or the automatic backups that are associated with that autonomous database, will be moved with that autonomous database as well.

05:21

Lois: Is there anything that I need to keep in mind when I’m moving an autonomous database between compartments? 

Sean: A couple of things to be aware of when doing this is, first of all, you must have the appropriate privileges in that compartment in order to move that autonomous database both from the source compartment to the target compartment. In addition to that, once the autonomous database is moved to this new compartment, any policies or anything that's defined in that compartment to govern the authorization and privileges of that said user in that compartment will be applied immediately to that new autonomous database that has been moved into that new compartment.

05:59

Nikita: Sean, I want to ask you about cloning in Autonomous Database. What are the different types of clones that can be created? 

Sean: It's possible to create a new Autonomous Database as a clone of an existing Autonomous Database. This can be done as a full copy of that existing Autonomous Database, or it can be done as a metadata copy, where the objects and tables are cloned, but they are empty. So there's no rows in the tables. And this clone can be taken from a live running Autonomous Database or even from a backup. So you can take a backup and clone that to a completely new database.

06:35

Lois: But why would you clone in the first place? What are the benefits of this? 

Sean: When cloning or when creating this clone, it can be created in a completely new compartment from where the source Autonomous Database was originally located. So it's a nice way of moving one database to another compartment to allow developers or another community of users to have access to that environment.

06:58

Nikita: I know that along with having a full clone, you can also have a refreshable clone. Can you tell us more about that? Who is responsible for this?

Sean: It's possible to create a refreshable clone from an Autonomous Database. And this is one that would be synced with that source database up to so many days.

The task of keeping that refreshable clone in sync with that source database rests upon the shoulders of the administrator. The administrator is the person who is responsible for performing that sync operation. Now, actually performing the operation is very simple, it's point and click. And it's an automated process from the database console.

And also be aware that refreshable clones can trail the source database or source Autonomous Database up to seven days. After that period of time, the refreshable clone, if it has not been refreshed or kept in sync with that source database, it will become a standalone, read-only copy of that original source database.

08:00

Nikita: Ok Sean, so if you had to give us the key takeaways on cloning an Autonomous Database, what would they be? 

Sean: It's very easy and a lot of flexibility when it comes to cloning an Autonomous Database. We have different models that you can take from a live running database instance with zero impact on your workload or from a backup. It can be a full copy, or it can be a metadata copy, as well as a refreshable, read-only clone of a source database.

08:33

Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all of which is available free to subscribers. So, get going! Pick a course of your choice, get certified, join the Oracle University Learning Community, and network with your peers.

If you are already an Oracle MyLearn user, go to MyLearn to begin your journey. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 

09:12

Nikita: Welcome back! Thank you, Sean, and hi Kay! I want to ask you about events and notifications in Autonomous Database. Where do they really come in handy? 

Kay: Events can be used for a variety of notifications, including admin password expiration, ADB services going down, and wallet expiration warnings. There's this service, and it's called the notifications service. It's part of OCI. And this service provides you with the ability to broadcast messages to distributed components using a publish and subscribe model. These notifications can be used to notify you when event rules or alarms are triggered or simply to directly publish a message.

In addition to this, there's also something that's called a topic. This is a communication channel for sending messages to subscribers in the topic. You can manage these topics and their subscriptions really easy. It's not hard to do at all.

10:14

Lois: Kay, I want to ask you about backing up Autonomous Databases. How does Autonomous Database handle backups?

Kay: Autonomous Database automatically backs up your database for you. The retention period for backups is 60 days. You can restore and recover your database to any point in time during this retention period.

You can initiate recovery for your Autonomous Database by using the cloud console or an API call. Autonomous Database automatically restores and recovers your database to the point in time that you specify.

In addition to a point in time recovery, we can also perform a restore from a specific backup set. 

10:59

Lois: Kay, you spoke about automatic backups, but what about manual backups? 

Kay: You can do manual backups using the cloud console, for example, if you want to take a backup say before a major change to make restoring and recovery faster. These manual backups are put in your cloud object storage bucket.

11:20

Nikita: Are there any special instructions that we need to follow when configuring a manual backup?

Kay: The manual backup configuration tasks are a one-time operation. Once this is configured, you can go ahead, trigger your manual backup any time you wish after that. When creating the object storage bucket for the manual backups, it is really important-- so I don't want you to forget-- that the name format for the bucket and the object storage follows this naming convention. It should be backup underscore database name. And it's not the display name here when I say database name.

12:00

Kay: In addition to that, the object name has to be all lowercase. So three rules. Backup underscore database name, and the specific database name is not the display name. It has to be in lowercase. Once you've created your object storage bucket to meet these rules, you then go ahead and set a database property. Default_backup_bucket. This points to the object storage URL and it's using the Swift protocol. Once you've got your object storage bucket mapped and you've created your mapping to the object storage location, you then need to go ahead and create a database credential inside your database. You may have already had this in place for other purposes, like maybe you were loading data, you were using Data Pump, et cetera. If you don't, you would need to create this specifically for your manual backups. Once you've done so, you can then go ahead and set your property to that default credential that you created. So once you follow these steps as I pointed out, you only have to do it one time. Once it's configured, you can go ahead and use it from now on for your manual backups.

13:21

Lois: Kay, the last topic I want to talk about before we let you go is Autonomous Data Guard. Can you tell us about it?

Kay: Autonomous Data Guard monitors the primary database, in other words, the database that you're using right now. 

Lois: So, if ADB goes down…

Kay: Then the standby instance will automatically become the primary instance. There's no manual intervention required. So failover from the primary database to that standby database I mentioned, it's completely seamless and it doesn't require any additional wallets to be downloaded or any new URLs to access APEX or Oracle Machine Learning. Even Oracle REST Data Services. All the URLs and all the wallets, everything that you need to authenticate, to connect to your database, they all remain the same for you if you have to failover to your standby database.

14:19

Lois: And what happens after a failover occurs?

Kay: After performing a failover, a new standby for your primary will automatically be provisioned. So in other words, in performing a failover your standby does become your new primary. Any new standby is made for that primary. I know, it's kind of interesting. So currently, the standby database is created in the same region as the primary database. For better resilience, if your database is provisioned, it would be available on AD1 or Availability Domain 1. My secondary, or my standby, would be provisioned on a different availability domain.

15:10

Nikita: But there’s also the possibility of manual failover, right? What are the differences between automatic and manual failover scenarios? When would you recommend using each?

Kay: So in the case of the automatic failover scenario following a disastrous situation, if the primary ADB becomes completely unavailable, the switchover button will turn to a failover button. Because remember, this is a disaster. Automatic failover is automatically triggered. There's no user action required. So if you're asleep and something happens, you're protected. There's no user action required, but automatic failover is allowed to succeed only when no data loss will occur.   15:57

Nikita: For manual failover scenarios in the rare case when an automatic failover is unsuccessful, the switchover button will become a failover button and the user can trigger a manual failover should they wish to do so. The system automatically recovers as much data as possible, minimizing any potential data loss. But you can see anywhere from a few seconds or minutes of data loss. Now, you should only perform a manual failover in a true disaster scenario, expecting the fact that a few minutes of potential data loss could occur, to ensure that your database is back online as soon as possible. 

16:44

Lois: Thank you so much, Kay. This conversation has been so educational for us. And thank you once again to Hannah and Sean. To learn more about Autonomous Database, head over to mylearn.oracle.com and search for the Oracle Autonomous Database Administration Workshop.

Nikita: Thanks for joining us today. In our next episode, we will discuss Autonomous Database on Dedicated Infrastructure. Until then, this is Nikita Abraham…

Lois: …and Lois Houston signing off.

17:12

That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In today’s digital economy, data is a form of capital. Given the mission-critical role that it has, having a robust data management strategy is now more crucial than ever.   Join Lois Houston and Nikita Abraham, along with Kay Malcolm, as they talk about the various Oracle Database offerings and discuss how to actually use them to efficiently manage data across a diverse but unified data tier.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community X (formerly Twitter): https://twitter.com/Oracle_Edu LinkedIn: https://www.linkedin.com/showcase/oracle-university/   Special thanks to Arijit Ghosh, David Wright, Ranbir Singh, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:

00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi there. If you’ve been following along with us these past few weeks, you’ll know we’ve been revisiting our most popular episodes of the year.  Lois: Right, and today’s episode is the last one of the Best of 2023 series. It’s a throwback to our conversation on Oracle’s Data Management strategy and offerings with Kay Malcolm, Senior Director of Database Product Management at Oracle. Nikita: We’d often heard Kay say that Oracle’s data management strategy is simply complete and completely simple. And so we began by asking her what she meant by that. 01:09 Kay: It's a fun play on words, right? App development paradigms are in a rapid state of transformation. Modern app development is simplifying and accelerating how you deploy applications. Also simplifying how data models and data analytics are used. Oracle data management embraces modern app development and transformations that go beyond technology changes. It presents a simply complete solution that is completely simple. Immediately you can see benefits of the easiest and most productive platform for developing and running modern app and analytics. 01:54 Kay: Oracle Database is a converged database that provides best of breed support for all different data models and workloads that you need. When you have converged support for application development, you eliminate data fragmentation. You can perform unique queries and transactions that span any data and create value across all data types and build into your applications.  02:24 Nikita: When you say all data types, this can include both structured and unstructured data, right? Kay: This also includes structured and unstructured data. The Oracle converged database has the best of breed for JSON, graph, and text while including other data types, relations, blockchain, spatial, and others. Now that we have the ability to access any data type, we have various workloads and converged data management that supports all modern transactional and analytical workloads. We have the unique ability to run any combination of workloads on any combination of data. Simply complete for analytics means the ability to include all of the transactions, including key value, IoT, or Internet of Things, along with operational data warehouse and lake and machine learning. 03:27 Kay: Oracle's decentralized database architecture makes decentralized apps simple to deploy and operate. This architecture makes it simple to use decentralized app development techniques like coding events, data events, API driven development, low code, and geo distribution. Autonomous Database or ADB now supports the Mongo database API adding more tools for architectural support. Autonomous Database or ADB has a set of automated tools to manage, provision, tune, and patch. It provides solutions for difficult database engineering with auto indexing and partitioning and is elastic. You can automatically scale up or down based on the workload. Autonomous Database is also very productive. It allows for focus on the data for solving business problems. ADB has self-service tools for analytics, data access, and it simplifies these difficult data engineering architectures. 04:43 Lois: OK…so can you tell us about running modern apps and analytics? Kay: Running applications means thinking about all the operational concerns and solving how to support mission-critical applications. Traditionally, this is where Oracle excels with high availability, security, operational solutions that have been proven over the years. Now, having developer tools and the ability to scale and reduce risk simplifies the development process without having to use complex sharding and data protection. Mission-critical capabilities that are needed for the applications are already provided in the functionality of the Oracle Data Management architecture. Disaster recovery, replication, backups, and security are all part of the Oracle Autonomous Database. 05:42 Kay: Even complex business-critical applications are supported by the operational security and availability of Oracle ADB. Transparently, it provides automated solutions for minimizing risk, dealing with complexity, and availability for all applications. Oracle's big picture data management strategy is simply complete and completely simple with the converged database, data management tools, and the best platform. It is focused on providing a platform that allows for modern app development across all data types, workloads, and development styles. It is completely scalable, available, and secure, leveraging the database technologies developed over several years. And it's available consistently across the environment. It is the simplest to use because of the available tools and running completely mission critical applications. 06:50 Nikita: Ah, so that’s how we come to… Kay: Simply complete and completely simple. Easy to remember and easy to incorporate into your existing architectures.  Lois: OK. So Kay, can you talk a little bit more about Autonomous Database? 07:04 Kay: Let's compare Autonomous Database to how you ran the database on premise. How you ran the database on the cloud using our earlier Cloud Services, Database Cloud Services, and Oracle Exadata Cloud Service. The key thing to understand is Autonomous Database, or ADB, is a fully managed service. We fully manage the infrastructure. We fully manage the database for you. In on premise, you manage everything-- the infrastructure, the database, everything. We also have a service in between that that we call a co-managed service. Here we manage the infrastructure, and you manage the database. That service is important for customers who are not yet up to 19c. Or they might be running a packaged application like E-Business Suite. But for the rest of you, ADB is really the place you want to go. 08:09 Nikita: And why is that? Kay: Because it's fully managed and, because it's fully managed, is a much, much lower cost way to go. So when you talk to your boss about why he wants to move to ADB, they often care about the bottom line. They want to know like, am I going to lower my costs? And with ADB, because we take care of a lot of the tedious chores that DBAs normally have to do and because we take care of best practices, configurations, we can do things at a really low cost.  08:49 Lois: Kay, what does it take for a customer to move to Oracle’s Autonomous Database?  Kay: We've got a tool that helps you look at your current database on prem. This tool will analyze what features you're using and let you know, hey, you know you're doing something that's not supported for ADB, for example. Like if you're running some release before 19c, we don't support it. If you're doing stuff like putting database tables in the system or sys schema, we don't support it. You know, there are a few things that very few customers do that we don't support. And this tool will flag those for you. And then the next step, it's pretty simple. You just use our Data Pump import/export tool to move your data out of your database on prem into the object store on the Cloud. And then you simply import-- you know how to use Data Pump to import-- the data off the file and the object store into the database. Then you're done. Pretty simple process. 09:57 Nikita: Do we assist our customers with data migration from on-prem to Cloud? Kay: More recently have come out with a new service on our Cloud called the Database Migration Service. With Autonomous Database Migration Service, you can just point us at your source database on prem or even on some other cloud. Whatever it is, we will take care of everything from there and move that, go through all the steps and move your database to ADB on the Cloud. Even better, we now are working with our Applications customers to make it really easy for them to move their packaged applications to Autonomous Database. The Oracle development teams that built JD Edwards, PeopleSoft, Siebel have now all certified that those packaged applications can run with Autonomous Database no problem. Our EBS team is working on it. And that'll be coming soon, sometime next year. 11:02 Lois: So, if I am an Apps customer, is there a special service for me? Kay: We have a fully managed service available on our Cloud that lets you take your entire application stack on the middle tier and the database tier, move it to our Cloud. Move the database part to Autonomous Database. And they will also manage your middle tier for you. 11:32 Want to get the inside scoop on Oracle University? Head on over to the all-new Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products and stay up-to-date with upcoming certification opportunities. If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. Join the community today! 12:11 Nikita: Welcome back! Kay, can you talk a bit about APEX?  Kay: We have this great tool called APEX or Application Express. We have a version of Autonomous Database just for any APEX application.  Well, APEX is a low-code tool. It is our low-code tool that lets you rapidly build data-driven applications where the data is in the Oracle Database, really easy and really rapidly. We estimate at least 10 times faster than doing traditional coding to build your applications. What we're seeing is much, much higher productivity than that. Sometimes 40, even 50 times faster coding. 13:01 Kay: Out of the box, it comes with really nice tools for building things-- your classical forms and reporting kinds of workloads. It gives you things like faceted search and capabilities to do things like see on an e-commerce website where you get to choose things like dimensions, like I want a product where the cost is in this range. And, you know, it might have some other attributes. And it can very quickly filter that data for you and return the best results. And it's a really nice tool for iterating. Now, if your user interface doesn't look quite right, it's very easy to tweak colors and backgrounds and themes. Another reason it's so productive is that the whole middle tier part of your application is fully automated for you. You don't have to do anything about connection management or state management. You don't have to worry about mapping data types from some other 3GL programming language to data types. All of that is done for you. The combination of ADB and APEX really rocks. 14:17 Lois: Do we have Extract, Transform, and Load capabilities in our ADB? Kay: We have ETL transformation tools. Again, they let you specify transformations in a drag-and-drop fashion on the screen. We have all sorts of other tools and, in the service, the full power of the converged analytic technologies, things like graph analytics, spatial analytics, machine learning. All of this is built into this new platform. Now, a big, new capability around machine learning is something that we call AutoML. That lets any data scientists give us a data set, tell us what the key feature is that they want to analyze, and what the predictions are. And we will come up with a machine learning model for them out of the box. Really that easy. Plus, we have the low-code tool APEX that I mentioned earlier. 15:17 Kay: So this environment is really powerful for doing more than traditional data warehouses. We can build data lakes. We are integrated with the object stores on Oracle Cloud and also on other clouds. And we can do massively parallel querying of data in the core database itself and the data lake. 15:38 Nikita: Beyond the database tech, there’s the business side, right? How easy do we make a customer’s path to ADB from a business standpoint, a decision-making standpoint? Kay: So if you're an existing Oracle customer, you have an existing Oracle Database license you're using on prem, we have something called BYOL, Bring Your Own License, to OCI. We have the Cloud Lift Service. This huge cloud engineering team across all regions of the world will help you move your existing on-prem database to ADB for free. 16:16 Kay: And then, finally, we announced fairly recently something called the Support Rewards Program. This is something our customers are really excited about. It lets them translate their spending on OCI to a reduction in their support bill. So if you're a customer using OCI, you get a $0.25 to $0.33 reward for every dollar you spend on Oracle's Cloud. You can then take that money from your rewards and apply it to your bill for customer support, for your technology support even, like the database. And this is exactly what customers want as they move their investment to the cloud. They want to lower the costs of paying for their on-prem support. Now, we've talked about money. This lowers costs greatly. So ADB has lots of value. But the big thing I think to think about is really that it lowers costs. It lowers that cost via automation, higher productivity, less downtime, all sorts of areas.   17:22 Lois: You make a very convincing case for ADB, Kay. Kay: ADB is a great place to go. Take those existing Oracle Databases you have. Move and modernize them to a modern cloud infrastructure that's going to give you all the benefits of cloud, including agility and lower cost. So on our Cloud, we have something called the Always Free Autonomous Database Service. This service lets you get your hands on ADB. Try it out for yourself. You don't have to believe what we claim about how great this technology is. And we have other technologies like Live Labs that you can find on developer.oracle.com/livelabs that lets you do all kinds of exercises on this Always Free ADB infrastructure. Really get your hands dirty. And see for yourself how productive it can be.  18:16 Nikita: Thanks, Kay, for telling us about ADB and our database offerings. To learn more about this, head over mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free Oracle Cloud Data Management Foundations Workshop. Lois: We hope you’ve enjoyed revisiting some of our most popular episodes these past few weeks. We’re kicking off the new year with a new season of the Oracle University Podcast. And this time around, it’ll be on Oracle Autonomous Database so make sure you don’t miss it. Until next week, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 18:52 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In this episode, Lois Houston and Nikita Abraham, along with Rohit Rahi, look at two important services that Oracle Cloud Infrastructure provides: Compute and Load Balancing. They also discuss the basics of instances.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community X (formerly Twitter): https://twitter.com/Oracle_Edu LinkedIn: https://www.linkedin.com/showcase/oracle-university/   Special thanks to Arijit Ghosh, Kiran BR, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:

00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there. You’re listening to our Best of 2023 series, where over the last few weeks, we’ve been revisiting our most popular episodes of the year. 00:47 Nikita: In today’s episode, which is #5 of 6, we’ll listen in to a conversation Lois and I had earlier this year with Rohit Rahi, Vice President of CSS OU Cloud Delivery, on OCI Compute and Load Balancing. We began by asking Rohit why one would use Load Balancer. Lois: So let’s get right to it! 01:06 Rohit: You would use Load Balancer to achieve high availability and also achieve scalability.  So typically the way Load Balancer works is, they're also referred to as Reverse Proxies, you would have a Load Balancer, which would be used accessed by multiple clients, various clients. And these clients would hit the Load Balancer, and the Load Balancer would proxy that traffic to the various backend servers. So in this way, it not only protects the various backend servers, but also provides high availability. In case a particular backend server is not available, the application can still be up and running. And then it also provides scalability because if lots of clients start hitting the Load Balancer, you could easily add more backend servers. And there are several other advanced capabilities like SSL termination and SSL passthrough and a lot of other advanced features.  So the first type of Load Balancer we have in OCI is a layer 7 Load Balancer. Layer 7 basically means it understands HTTP and HTTPS. That's the OSI model. And then there are various capabilities available here.  02:13 Nikita: The Load Balancer comes in two different shapes, right? Can you tell us a little about that? Rohit: One is called a flexible shape where you define the minimum and the maximum and you define the range. And your Load Balancer can achieve any kind of-- support any kind of traffic in that particular range, going from 10 Mbps all the way to 8 Gbps.  The second kind of shape is called dynamic where you predefine the shapes. So you have micro, small, medium, large, and you predefine the shape. And you don't have to warm up your Load Balancer. If the traffic comes to that particular shape, the Load Balancer automatically scales.  02:53 Rohit: You can always do a public and a private Load Balancer. Public means Load Balancer is available on the web. Private means your multiple tiers, like a web tier, can talk to your database tier and balance the traffic between them, but both tiers don't have to be public.  A Load Balancer is highly available, highly scalable by design. 03:12 Lois: And what about the second type of Load Balancer? Rohit: The second kind of Load Balancer we have in OCI is called the Network Load Balancer. And as the name specify, Network Load Balancer operates at layer 4, layer 3, and layer 4 so it understands TCP, UDP, also supports ICMP. Again, like HTTP Load Balancer, it has both public and a private option, so you could create a public Network Load Balancer or a private Network Load Balancer. It's highly available, highly scalable, all those features are supported.  03:42 Nikita: Now, why would you use Network Load Balancer over an HTTP Load Balancer?  Rohit: The primary reason you would use it is it's much faster than HTTP Load Balancer. It has much lower latency. So if performance is a key criteria for you, go with Network Load Balancer.  On the contrary, the HTTP Load Balancer has higher level intelligence because it can look at the packets, it can inspect the packets, and it gets that intelligence. So if you're looking for that kind of routing intelligence, then go with HTTP Load Balancer.  04:15 Rohit: So OCI Compute service provides you virtual machines and bare metal servers to meet your compute and application requirements. The three defining characteristics of this service include this scalability, high performance, and lower pricing. So the first thing in the OCI Compute service is you have this notion of flexible shape. What does it mean? Well, it means you could choose your own course, your CPU processors, and you could also choose your own memory. Literally, there are thousands and thousands of configurations you can choose from. 04:49 Lois: But what’s the use of doing this?  Rohit: The use of doing this is you could select the right machine type by using our flexible shapes.  And in the cloud, there's this notion of T-shirt sizing. So you have a small, medium, large kind of shapes, and your application has to fit those shapes. And sometimes you overprovision or underprovision, and you have to go through that painful process of changing your machine types. We hope with this flexible shapes, you don't have to do that.  05:20 Rohit: If you still want to use the traditional approach, we have virtual machines, we have bare metal servers, and we have dedicated host. And you could use either one of them or all of them. And bare metal servers basically means you get a full machine, a full server which is completely dedicated to you. Dedicated host basically means that you get a full dedicated bare metal machine. But on top of that, you could run virtual machines.  Not only this, but OCI is only one of the two cloud providers to provide you options on processors. So you can run AMD-based instances, you could run Intel-based instances, and you could also run Arm-based instances-- are really a powerful thing for mobile computing. The phones you are using today are probably running on Arm processors. Now, Arm is coming into the data centers. 06:16 Have something interesting to share with the Oracle University Learning Community? Present your topic at an exclusive community event. Help yourself by helping others. Start building your reputation and personal brand today.  If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 06:48 Nikita: What can you tell us about the pricing of this, Rohit? Rohit: On the pricing side, the service implements pay-as-you-go pricing. We are 50% cheaper than any other cloud out there, just to begin with. And not only that, you could use something like a Preemptable VMs to reduce your cost by more than 50% from your regular instances.  Preemptable VMs are low cost, short lived VMs suited for batch jobs and fault tolerant workloads. These are similar to regular instances, but priced 50% lower. So you can use them to reduce your cost further. So when we say an instance, what we mean is a compute host. And it has several dependencies. So let's look at them.  07:31 Rohit: So you have an Oracle Cloud region here. A region is comprised of multiple ADs. An AD is nothing but a data center. The first dependency the compute service has or compute hosts have is on Virtual Cloud Network. So in order to spin up a compute instance, you need a Virtual Cloud Network. You have a network divided into smaller portions called subnets. So you have a subnetwork here, and you need to create these before you can spin up a compute host.  08:00 Rohit: Now you can spin up a compute host. It's a physical construct. Networking is a virtual construct. So how are they related? Within a compute host, you have a physical network interface card, and you virtualize that card. We give you this virtual NIC. And that virtual NIC is placed inside the subnet. And that's the association for the compute host. And that's where the private IP for the compute host comes from, because every compute host or VM you are running, or a bare metal machine, has a private IP address.  Now, there is another set of dependency the compute instances have, and that's to the boot volume and the boot disk and the block volumes.  08:42 Lois: What does that mean, exactly?  Rohit: Well, each of these compute hosts you are spinning up has an operating system. And the image that's used to launch an instance determines its operating system and other software. So you have this concept of an image that comes from this network storage disk called a boot disk. So it doesn't stay on the compute host, it's actually living on the network somewhere.  And you also have data, like file systems, etc. You're working on the compute instances. They also live on the network. So there is the data disks and operating system disks together. There's a service called block volume service which the compute host uses to run its operating system and run its data disks. Now, these are remote storage.  09:33 Rohit: There is one more feature which is really relevant when you are talking about compute instances, and that's live migration. We know that computers fail all the time. So how do we make sure that whatever compute host you are running is always up and running, itself? So we have this feature called live migrate. And the idea here is if one of the compute hosts goes down, there's a problem, we would migrate your VM to another host in our data center, and it will be transparent to you. There are multiple options you provide-- whether opt-in or opt-out-- you can choose from. But the idea is we migrate your virtual machines so you can live-migrate between hosts without rebooting. This keeps your applications running even during maintenance events. To achieve this in your own data centers is a not-so-trivial task, but we make that seamless within OCI.  10:22 Nikita: Thanks for that, Rohit. To learn more about OCI, please visit mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free OCI Foundations training.  Lois: You will find skill checks that you can take throughout the course to ensure that you are on the right track. Nikita: We hope you enjoyed that conversation. Join us next week for our final throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 10:54 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.