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
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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
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