How do all your devices connect and stay safe in the cloud? In this episode, Lois Houston and Nikita Abraham talk with OCI instructors about the basics of how networks work and the simple steps that help protect them. You'll learn how information gets from one place to another, why tools like switches, routers, and firewalls are important, and what goes into keeping access secure. The discussion also covers how organizations decide who can enter their systems and how they keep track of activity. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:38

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, Team Lead: Editorial Services.

Nikita: Hi everyone! In the last episode, we spoke about local area networks and domain name systems. Today, we'll continue our conversation on the fundamentals of networking, covering a variety of important topics.

01:03

Lois: That's right, Niki. And before we close, we'll also touch on the basics of security. Joining us today are two OCI instructors from Oracle University: Sergio Castro and Orlando Gentil. So glad to have you both with us guys. Sergio, with so many users and devices connecting to the internet, how do we make sure everyone can get online? Can you break down what Network Address Translation, or NAT, does to help with this?

Sergio: The world population is bigger than 4.3 billion people. That means that if we were to interconnect every single human into the internet, we will not have enough addresses. And not all of us are connected to the internet, but those of us who are, you know that we have more than one device at our disposal. We might have a computer, a laptop, mobile phones, you name it. And all of them need IP addresses. So that's why Network Address Translation exists because it translates your communication from a private IP to a public IP address. That's the main purpose: translate.

02:18

Nikita: Okay, so with NAT handling the IP translation, how do we ensure that the right data reaches the right device within a network? Or to put it differently, what directs external traffic to specific devices inside a network?

Sergio: Port forwarding works in a reverse way to Network Address Translation. So, let's assume that this PC here, you want to turn it into a web server. So, people from the outside, customers from the outside of your local area network, will access your PC web server. Let's say that it's an online store.

Now all of these devices are using the same public IP address. So how would the traffic be routed specifically to this PC and not to the camera or to the laptop, which is not a web server, or to your IP TV? So, this is where port forwarding comes into play. Basically, whenever it detects a request coming to port, it will route it and forward that request to your PC.

It will allow anybody, any external device that wants to access this particular one, this particular web server, for the session to be established. So, it's a permission that you're allowing to this PC and only to this PC. The other devices will still be isolated from that list. That's what port forwarding is.

03:48

Lois: Sergio, let's talk about networking devices. What are some of the key ones, and what role do they play in connecting everything together?

Sergio: There's plenty of devices for interconnectivity. These are devices that are different from the actual compute instances, virtual machines, cameras, and IPTV. These are for interconnecting networks. And they have several functionalities.

04:11

Nikita: Yeah, I often hear about a default gateway. Could you explain what that is and why it's essential for a network to function smoothly?

Sergio: A gateway is basically where a web browser goes and asks a service from a web server. We have a gateway in the middle that will take us to that web server. So that's basically is the router. A gateway doesn't necessarily have to be a router. It depends on what device you're addressing at a particular configuration.

So, a gateway is a connectivity device that connects two different networks. That's basically the functionality.

04:47

Lois: Ok. And when does one use a default gateway?

Sergio: When you do not have a specific route that is targeting a specific router. You might have more than one router in your network, connecting to different other local area networks. You might have a route that will take you to local area network B. And then you might have another router that is connecting you to the internet. So, if you don't have a specific route that will take you to local area network B, then it's going to be utilizing the default gateway.

It directs data packets to other networks when no specific route is known. In general terms, the default gateway, again, it doesn't have to be a router. It can be any devices.

05:34

Nikita: Could you give us a real-world example, maybe comparing a few of these devices in action, so we can see how they work together in a typical network?

Sergio: For example, we have the hub. And the hub operates at the physical layer or layer 1.

And then we have the switch. And the switch operates at layer 2. And we also have the router. And the router operates at layer 3. So, what's the big difference between these devices and the layers that they operate in? So, hubs work in the physical layer of the OSI model. And basically, it is for connecting multiple devices and making them act as a single network segment.

Now, the switch operates at the data link layer and is basically a repeater, and is used for filtering content by reading the addresses of the source and destination. And these are the MAC addresses that I'm talking about. So, it reads where the packet is coming from and where is it going to at the local area network level.

It connects multiple network segments. And each port is connected to a different segment. And the router is used for routing outside of your local area network, performs traffic directing functions on the internet.

A data packet is typically forwarded from one router to another through different networks until it reaches its destination node. The switch connects multiple network segments. And each port of the switch is connected to a different segment.

And the router performs traffic directing functions on the internet. It takes data from one router to another, and it works at the TCP/IP network layer or internet layer.

07:34

Lois: Sergio, what kind of devices help secure a network from external threats?

Sergio: The network firewall is used as a security device that acts as a barrier between a trusted internal network and an untrusted external network, such as the internet. The network firewall is the first line of defense for traffic that passes in and out of your network.

The firewall examines traffic to ensure that it meets the security requirements set by your organization, or allowing, or blocking traffic based on set criteria. And the main benefit is that it improves security for access management and network visibility.

08:23

Are you keen to stay ahead in today's fast-paced world? We've got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you're always in the know, we offer New Features courses that give you an insider's look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started.

08:48

Nikita: Welcome back! Sergio, how do networks manage who can and can't enter based on certain permissions and criteria?

Sergio: The access control list is like the gatekeeper into your local area network. Think about the access control list as the visa on your passport, assuming that the country is your local area network. Now, when you have a passport, you might get a visa that allows you to go into a certain country.

So the access control list is a list of rules that defines which users, groups, or systems have permissions to access specific resources on your networks.

It is a gatekeeper, that is going to specify who's allowed and who's denied. If you don't have a visa to go into a specific country, then you are denied. Similar here, if you are not part of the rule, if the service that you're trying to access is not part of the rules, then you cannot get in.

09:49

Lois: That's a great analogy, Sergio. Now, let's turn our attention to one of the core elements of network security: authentication and authorization. Orlando, can you explain why authentication and authorization are such crucial aspects of a secure cloud network?

Orlando: Security is one of the most critical pillars in modern IT systems. Whether you are running a small web app or managing global infrastructure, every secure system starts by answering two key questions. Who are you, and what are you allowed to do? This is the essence of authentication and authorization.

Authentication is the first step in access control. It's how a system verifies that you are who you claim to be. Think of it like showing your driver's license at a security checkpoint. The guard checks your photo and personal details to confirm your identity. In IT systems, the same process happens using one or more of these factors.

It will ask you for something you know, like a password. It will ask you for something that you have, like a security token, or it will ask you for something that you are, like a fingerprint. An identity does not refer to just a person. It's any actor, human or not, that interacts with your systems. Users are straightforward, think employees logging into a dashboard. But services and machines are equally important. A backend API may need to read data from a database, or a virtual machine may need to download updates. Treating these non-human identities with the same rigor as human ones helps prevent unauthorized access and improves visibility and security. After confirming your identity, can the system move on to deciding what you're allowed to access? That's where authorization comes in. Once authentication confirms who you are, authorization determines what you are allowed to do.

Sticking with the driver's license analogy, you've shown your license and proven your identity, but that doesn't mean that you can drive anything anywhere. Your license class might let you drive a car, not a motorcycle or a truck. It might be valid in your country, but not in others. Similarly, in IT systems, authorization defines what actions you can take and on which resources. This is usually controlled by policies and roles assigned to your identity. It ensures that users or services only get access to the things they are explicitly allowed to interact with.

12:47

Nikita: How can organizations ensure secure access across their systems, especially when managing multiple users and resources?

Orlando: Identity and Access Management governs who can do what in our systems. Individually, authentication verifies identity and authorization grants access. However, managing these processes at scale across countless users and resources becomes a complex challenge. That's where Identity and Access Management, or IAM, comes in.

IAM is an overarching framework that centralizes and orchestrates both authentication and authorization, along with other critical functions, to ensure secure and efficient access to resources.

13:35

Lois: And what are the key components and methods that make up a robust IAM system?

Orlando: User management, a core component of IAM, provides a centralized Identity Management system for all user accounts and their attributes, ensuring consistency across applications.

Key functions include user provisioning and deprovisioning, automating account creation for new users, and timely removal upon departure or role changes. It also covers the full user account lifecycle management, including password policies and account recovery. Lastly, user management often involves directory services integration to unify user information. Access management is about defining access permissions, specifically what actions users can perform and which resources they can access.

A common approach is role-based access control, or RBAC, where permissions are assigned to roles and users inherit those permissions by being assigned to roles. For more granular control, policy-based access control allows for rules based on specific attributes. Crucially, access management enforces the principle of least privilege, granting only the minimum necessary access, and supports segregation of duties to prevent conflicts of interest.

For authentication, IAM systems support various methods. Single-factor authentication, relying on just one piece of evidence like a password, offers basic security. However, multi-factor authentication significantly boosts security by requiring two or more distinct verification types, such as a password, plus a one-time code. We also have biometric authentication, using unique physical traits and token-based authentication, common for API and web services.

15:46

Lois: Orlando, when it comes to security, it's not just about who can access what, but also about keeping track of it all. How does auditing and reporting maintain compliance?

Orlando: Auditing and reporting are essential for security and compliance. This involves tracking user activities, logging all access attempts and permission changes. It's vital for meeting compliance and regulatory requirements, allowing you to generate reports for audits. Auditing also aids in security incident detection by identifying unusual activities and providing data for forensic analysis after an incident.

Lastly, it offers performance and usage analytics to help optimize your IAM system.

16:35

Nikita: That was an incredibly informative conversation. Thank you, Sergio and Orlando, for sharing your expertise with us. If you'd like to dive deeper into these concepts, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course.

Lois: I agree! This was such a great conversation! Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

16:58

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 unpack the basics of cloud networking and the Domain Name System (DNS). You'll learn how local and virtual networks connect our devices, and how DNS seamlessly translates familiar names like oracle.com into addresses computers understand. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------- Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:38

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, Team Lead: Editorial Services.

Nikita: Hi everyone! For the last few weeks, we've been talking about different aspects of cloud data centers. Today, we're focusing on something that's absolutely key to how everything works in the cloud: networking and domain name systems.

01:04

Lois: And to guide us through it, we've got Sergio Castro, Senior Principal OCI Instructor at Oracle University. We'll start by trying to understand why networking is so crucial and how it connects everything behind the scenes. Sergio, could you explain what networking means in simple terms, especially for folks new to cloud tech?

Sergio: Networking is the backbone of cloud computing. It is a fundamental service because it provides the infrastructure for connecting users, applications, and resources within a cloud environment.

It basically enables data transfers. It facilitates remote access. And ensures that cloud services are accessible to users. This provided that these users have the correct credentials.

01:50

Nikita: Ok, can you walk us through how a typical network operates?

Sergio: In networking, typically starts with the local area network. Basically, networking is a crucial component for any IT service because it's the foundation for the architecture framework of any of the services that we consume today.

So, a network is two or more computers interconnected to each other. And not necessarily it needs to be a computer. It can be another device such as a printer or an IP TV or an IP phone or an IP camera. Many devices can be part of a local area network.

And a local area network can be very small. Like I mentioned before, two or more computers, or it could grow into a very robust and complicated set of interconnected networks. And if that happens, then it can become very expensive as well.

Cloud networking, it's the Achilles heel for many of the database administrators, programmers, quality assurance engineers, any IT other than a network administrator. Actually, when the network starts to grow, managing access and permissions and implementing robust security measures, this coupled with the critical importance of reliable, and secure performance, can create significant hurdles.

03:21

Nikita: What are the different types of networks we have?

Sergio: A local area network is basically in one building. It covers… it can be maybe two buildings that are in close proximity in a small campus, but typically it's very small by definition, and they're all interconnected to each other via one router, typically.

A metropolitan area network is a typical network that spans into a city or a metro area, hence the name metropolitan area network. So, one building can be on one edge of the city and the other building can be at the other edge of the city, and they are interconnected by a digital circuit typically. So that's the case. It's more than one building, and the separation of those buildings is considerable. It can go into several miles.

And a wide area network is a network that spans multiple cities, states, countries, even international.

04:23

Lois: I think we'll focus on the local area network for today's conversation. Could you give us a real-world example, maybe what a home office network setup looks like?

Sergio: If you are accessing this session from your home office or from your office or corporate office even, but a home office or a home network, typically, you have a router that is being provided to you by the internet vendor—the internet service provider.

And then you have your laptop or your computer, your PC connected to that router. And then you might have other devices either connected via cable—ethernet cable—or Wi-Fi. And the interconnectivity within that small building is what makes a local area network.

And it looks very similar once you move on into a corporate office. Again, it's two or more computers interconnected. That's what makes a local area network. In a corporate office, the difference with a home office or your home is that you have many more computers.

And because you have many more computers, that local area network might be divided into subnets. And for that, you need a switch. So, you have additional devices like a switch and a firewall and the router. And then you might have a server as well.

So that's the local area network. Two or more computers. And local area networks are capable of high speeds because they are in close proximity to each other.

05:59

Nikita: Ok… so obviously a local area network has several different components. Let's break them down. What's a client, what's a server, and how do they interact?

Sergio: A client basically is a requester of a service. Like when you hop into your browser and then you want to go to a website, for example, oracle.com, you type www.oracle.com, you are requesting a service from a server.

And that server typically resides in a data center like oracle.com under the Oracle domain is a big data center with many interconnected servers. Interconnected so they can concurrently serve multiple millions of requests coming into www.oracle.com at the same time.

So, servers provide services to client computers. So basically, that's the relation. A client requests a service and the server provides that service.

07:03

Lois: And what does that client-server setup actually look like?

Sergio: So, let's continue with our example of a web browser requesting a service from a web server.

So, in this case, the physical computer is the server. And then it has a software running on it. And that makes it a web server. So, once you type www.oracle.com, it sends the request and the request is received. And provided that everything's configured correctly and that there are no typos, then it will provide a response and basically give the view of the website.

And that's obviously in the local area network, maybe quality assurance when they were testing this for going live. But when it goes live, then you have the internet in the middle. And the internet in the middle then have many routers, hubs, switches.

08:04

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08:31

Nikita: Welcome back! Sergio, would this client-server model also apply to my devices at home?

Sergio: In your own local area network, you have client server even without noticing. For example, let's go back to our home office example. What happens if we add another laptop into the scenario?

Then all of these devices, they need a way for them to communicate. And for that, they have an IP address. And who provides that IP address?

The minute that you add, the other device is going to send a request to the router. The router, we call it router, but it has multiple functions like the mobile device, the handheld device that we call smartphone. It has many functions like camera and calendar and many other functionalities.

The router has an additional functionality called the dynamic host configuration protocol at DHCP server. So basically, the laptop requests, hey, give me an IP address, and then the router or the DHCP server replies, here's your IP address. And it's going to be a different one. So, they don't overlap. So that's an example of client server.

09:48

Lois: And where do virtual networks fit into all this?

Sergio: A virtual network is basically, a software version of the physical network. It looks and feels exactly as a physical network does. We do have a path or a communication, in this case, in the physical network, you have either Wi-Fi or you have internet cable. And then you add your workstations or devices on top of that. And then you might create subnets.

So, in a software-defined network or in a virtual network, you have a software-defined connectivity, physical cable and all of that. Everything is software-defined. And it looks exactly the same, except that everything is software.

In a software or a virtual network, you can communicate with a physical network as if that software or that virtual network was another physical network. Again, this is a software network or a software-defined network, a virtual network, no longer a physical network.

10:58

Lois: Let's switch gears a little and talk about Domain Name Systems. Sergio, can you explain what DNS is, and why it's important when we browse the web?

Sergio: DNS is the global database for internet addressing. The DNS plays a very important role on the internet. And many internet services are closely related to DNS.

The main functionality of DNS is to translate easy-to-remember names into IP addresses. Some IP addresses might be very easy to remember. But however, if you have many of them, then it's easier to remember oracle.com or ucla.edu or navy.mil for military or eds.org for organization or gobierno.mx for Mexico. So that's the main feature of the DNS.

It's very similar to a mobile phone to the contacts application in your mobile phone, because the contacts application maps names to phone numbers. It's easier to remember Bob's phone than 555-123-4567. So, it's easier to remember the name of the persons in your contacts list, like it is easier to remember, as previously mentioned, oracle.com than 138.1.33.162.

Again, 138.1.33.162 might be easy for you to remember if that's the only one that you need to remember. But if you have 20, 40, 50, like we do with phone numbers, it's easier to remember oracle.com or ucla.edu. And this is essential, this mapping, again, because we work with names it's easier for us to remember.

However, the fact is that computers, they still need to use IP addresses. And remember that this is the decimal representation of the binary number. It's a lot harder for us to remember the 32 bits or each one of the octets in binary. So that's the main purpose of DNS.

Now the big difference is that the contact list in a cell phone is unique to that individual phone. However, DNS is global. It applies to everybody in the world. Anybody typing oracle.com will translate that into 138.1.33.162.

Now this is an actual IP address of oracle.com. Oracle.com has many IP addresses. If you ping oracle.com, chances are that this is one of the many addresses that maps to oracle.com.

13:50

Nikita: You mentioned that a domain name like oracle.com can have many IP addresses. So how does DNS help my computer find the right one?

Sergio: So, let's say that you want to look for www.example.com, how do you do that? So, you type in your computer instance or in your terminal, in your laptop, in your computer, you type in your browser "www.example.com."

If the browser doesn't have that information in cache, then it's going to first ask your DNS server, the one that you have assigned and indicating in your browser's configuration. And if the DNS server then it will relate that the information is 96.7.128.198. This address is real, and your browser will go to this address once you type www.example.com.

14:50

Nikita: But what happens if the browser doesn't know the address?

Sergio: This is where it gets interesting. Your browser wants to go to www.example.com. And it's going to go and look within its cache. If it doesn't have it, then the first step is to go ahead to your DNS server and ask them, hey, if you don't know this address, go ahead and find out.

So, it goes to the root server. All the servers are administrated by IANA. And it's going to send the information, hey, what's the IP address for www.example.com?

And if the root server doesn't know it, it's going to let you know, hey, ask the top-level domain name server, in this case, the .com. It's a top-level domain name server.

So, you go ahead and ask this top-level domain name server to do that for you. In this case, again, the .com and you asked, hey, what's the IP address for example.com?

And if the top-level domain name server doesn't know, it's going to ask you, hey, ask example.com. And example.com is actually within the customer's domain. And then based on these instructions you ask, what is the IP address for www.example.com?

So, it will provide you with the IP address. And once your DNS server has the IP address, then it's going to relate to your web browser. And this is where your web browser actually reaches 96.7.128.198. Very interesting, isn't it?

16:39

Lois: Absolutely! Sergio, you mentioned top-level domain names. What are they and how are they useful?

Sergio: A top level domain is the rightmost segment of a domain name, and it's located after the last visible dot in the domain name.

So oracle.com or cloud.oracle.com is a domain name. So, .com is a top-level domain.

And the purpose of the top-level domain is to recognize certain elements of a website. This top-level domain indicates that this is a commercial site. Now, .edu, for example, is a top-level domain name for higher education.

We also have .org for nonprofit organizations, .net for network service providers. And we also have country specific. .ca for Canadian websites, .it for Italian websites. Now .it, a lot of companies that are in the information technology business utilizes this one to indicate that they're in information technology.

There's also the .us. And for US companies, most of the time this is optional. .com, .org, .net is understood that they are from the US.

Now if .com is a top-level domain name, what is that .oracle in cloud? So, Oracle is the second-level domain name. And in this case, Cloud is the third-level domain name.

And lately you've been seeing a lot more top-level domain names. These are the classic ones. But now you get .AI, .media, .comedy, .people, and so on and so forth. You have many, many, even companies now have the option of registering their company name as the top-level domain name.

18:39

Nikita: Thank you, Sergio, for this deep dive into local area networks and domain name systems. If you want to learn about the topics we covered today, go to mylearn.oracle.com and search for the Cloud Tech Jumpstart course.

Lois: And don't forget to join us next week for another episode on networking essentials. Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

19:01

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 break down the differences between Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. The conversation explores how each framework influences control, cost efficiency, expansion, reliability, and contingency planning. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------- Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:38

Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hey there! Last week, we spoke about how hypervisors, virtual machines, and containers have transformed data centers. Today, we're moving on to something just as important—the main cloud models that drive modern cloud computing.

Nikita: Orlando Gentil, Principal OCI Instructor at Oracle University, joins us once again for part four of our discussion on cloud data centers.

01:14

Lois: Hi Orlando! Glad to have you with us today. Can you walk us through the different types of cloud models?

Orlando: These are commonly categorized into three main service models: Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service.

Let's use the idea of getting around town to understand cloud service models. IaaS is like renting a car. You don't own the car, but you control where it goes, how fast, and when to stop.

In cloud terms, the provider gives you the infrastructure—virtual machines, storage, and networking—but you manage everything on top—the OS, middleware, runtime, and application. Thus, it's like using a shuttle service. You bring your bags—your code, pick your destination—your app requirements, but someone else drives and maintains the vehicle.

You don't worry about the engine, fuel, or routing planning. That's the platform's job. Your focus stays on development and deployment, not on servers or patching. SaaS is like ordering a taxi. You say where you want to go and everything else is handled for you. It's the full-service experience.

In the cloud, SaaS is software UXs over the web—Email, CRM, project management. No infrastructure, no updates, just productivity.

02:45

Nikita: Ok. How do the trade-offs between control and convenience differ across SaaS, PaaS, and IaaS?

Orlando: With IaaS, much like renting a car, you gain high control. You are managing components like the operating system, runtime, your applications, and your data. In return, the provider expertly handles the underlying virtual machines, storage, and networking. This model gives you immense flexibility.

Moving to PaaS, our shuttle service, you shift to a medium level of control but gain significantly higher convenience. Your primary focus remains on your application code and data. The provider now takes on the heavy lifting of managing the runtime environment, the operating system, the servers themselves, and even the scaling.

Finally, SaaS, our taxi service, offers the highest convenience with the lowest control level. Here, your responsibility is essentially just using the application and managing your specific configurations or data within it. The cloud provider manages absolutely everything else—the entire infrastructure, the platform, and the application itself.

04:05

Nikita: One of the top concerns for cloud users is cost optimization. How can we manage this?

Orlando: Each cloud service model offers distinct strategies to help you manage and reduce your spending effectively, as well as different factors that drives those costs.

For Infrastructure-as-a-Service, where you have more control, optimization largely revolves around smart resource management. This means rightsizing your VMs, ensuring they are not overprovisioned, and actively turning off idle resources when not in use. Leveraging preemptible or spot instances for flexible workloads can also significantly cut costs. Your charges here are directly tied to your compute, storage, and network usage, so efficiency is key.

Moving to Platform-as-a-Service, where the platform is managed for you, optimization shifts slightly. Strategies include choosing scalable platforms that can efficiently handle fluctuating demand, opting for consumption-based pricing where available, and diligently optimizing your runtime usage to minimize processing time.

Costs in PaaS are typically based on your application usage, runtime hours, and storage consumed. Finally, for Software-as-a-Service where you can consume a ready-to-use application, cost optimization centers on licensing and usage.

This involves consolidating tools to avoid redundant subscriptions, selecting usage-based plans if they align better with your needs, and crucially, eliminating any unused license. SaaS costs are generally based on subscription or per user fees. Understanding these nuances is essential for effective cloud financial management.

06:05

Lois: Ok. And what about scalability? How does each model handle the ability to grow and shrink with demand, without needing manual hardware changes?

Orlando: How you achieve and manage that scalability varies significantly across our three service models. For Infrastructure-as-a-Service, you have the most direct control over scaling. You can implement manual or auto scaling by adding or removing virtual machines as needed, often leveraging load balancers to distribute traffic. In this model, you configure the scaling policies and parameters based on your specific workload.

Moving to Platform-as-a-Service, the scaling becomes more automated and elastic. The platform automatically adjusts resources based on your application's demand, allowing it to seamlessly handle traffic spikes or dips. Here, the provider manages the underlying scaling behavior, freeing you from that operational burden.

Finally, with Software-as-a-Service, scalability is largely abstracted and invisible to the user. The application scales automatically in the background, with the entire process fully managed by the provider. As a user, you simply benefit from the application's ability to handle millions of users without ever needing to worry about the infrastructure. Understanding these scaling differences is crucial for selecting the right model for your application's need.

07:45

Join the Oracle University Learning Community and tap into a vibrant network of over 1 million members, including Oracle experts and fellow learners. This dynamic community is the perfect place to grow your skills, connect with likeminded learners, and celebrate your successes. As a MyLearn subscriber, you have access to engage with your fellow learners and participate in activities in the community. Visit community.oracle.com/ou to check things out today!

08:18

Nikita: Welcome back! We've talked about cost optimization and scalability in cloud environments. But what about ensuring availability? How does that work?

Orlando: Availability refers to the ability of a system or service to remain accessible in operational, even in the face of failures or extremely high demand.

The approach of achieving and managing availability, and crucially, your role versus the provider's, differs greatly across each model. With Infrastructure-as-a-Service, you have the most direct control over your availability strategy. You will be responsible for designing an architecture that includes redundant VMs, deploying load balancers, and potentially even multi-region setups for disaster recovery.

Your specific roles involves designing this architecture and managing your failover process and data backups. The provider's role, in turn, is to deliver the underlying infrastructure with defined service level agreements, SLAs, and health monitoring.

For Platform-as-a-Service, the platform itself offers a higher degree of built-in, high availability, and automated failover. While the provider maintains the runtime platform's availability, your role shifts.

You need to ensure your application's logic is designed to gracefully handle retries and potential transient failures that might occur. Finally, with Software-as-a-Service, availability is almost entirely handled for you. The provider ensures fully abstracted redundancy and failover behind the scenes.

Your role becomes largely minimal, often just involving a specific application's configurations. The provider is entirely responsible for the full application uptime and the underlying high availability infrastructure. Understanding these distinct roles in ensuring availability is essential for setting expectations and designing your cloud strategy efficiently.

10:32

Lois: Building on availability, let's talk Disaster Recovery.

Orlando: DR is about ensuring your systems and data can be recovered and brought back online in the event of a significant failure, whether it's a hardware crash, a natural disaster, or even human error.

Just like the other aspects, the strategy and responsibilities for DR vary significantly across the cloud service models. For Infrastructure-as-a Service, you have the most direct involvement in your DR strategy.

You need to design and execute custom DR plans. This involves leveraging capabilities like multi-region backups, taking VM snapshots, and setting up failover clusters. A real-world example might be using Oracle Cloud compute to replicate your VMs to a secondary region with block volume backups to ensure business continuity. Essentially, you manage your entire DR process here.

Moving to Platform-as-a-Service, disaster recovery becomes a shared responsibility. The platform itself offers built-in redundancy and provide APIs for backup and restore. Your role will be to configure the application-level recovery and ensure your data is backed up appropriately, while the provider handles the underlying infrastructure's DR capability.

An example could be Azure app service, Oracle APEX applications, where your apps are redeployed from source control like Git after an incident. Finally, with Software-as-a-Service, disaster recovery is almost entirely vendor managed.

The provider takes full responsibility, offering features like auto replication and continuous backup, often backed by specific Recovery Point Objective (RPO) and Recovery Time Objective (RTO) SLAs.

A common example is how Microsoft 365 or Salesforce manage user data backups in restoration. It's all handled seamlessly by the provider without your direct intervention. Understanding these different approaches to DR is crucial for defining your own business continuity plans in the cloud.

12:59

Lois: Thank you, Orlando, for this insightful discussion. To recap, we spoke about the three main cloud models: IaaS, PaaS, and SaaS, and how each one offers a different mix of control and convenience, impacting cost, scalability, availability, and recovery.

Nikita: Yeah, hopefully this helps you pick the right cloud solution for your needs. If you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. In our next episode, we'll take a close look at the essentials of networking. Until then, this is Nikita Abraham…

Lois: And Lois Houston, signing off!

13:39

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.

Have you ever considered how a single server can support countless applications and workloads at once? In this episode, hosts Lois Houston and Nikita Abraham explore the sophisticated technologies that make this possible in modern cloud data centers. They discuss the roles of hypervisors, virtual machines, and containers, explaining how these innovations enable efficient resource sharing, robust security, and greater flexibility for organizations. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------- Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:38

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, Team Lead: Editorial Services.

Nikita: Hi everyone! For the last two weeks, we've been talking about different aspects of cloud data centers. In this episode, Orlando Gentil, Principal OCI Instructor at Oracle University, joins us once again to discuss how virtualization, through hypervisors, virtual machines, and containers, has transformed data centers.

01:11

Lois: That's right, Niki. We'll begin with a quick look at the history of virtualization and why it became so widely adopted. Orlando, what can you tell us about that?

Orlando: To truly grasp the power of virtualization, it's helpful to understand its journey from its humble beginnings with mainframes to its pivotal role in today's cloud computing landscape. It might surprise you, but virtualization isn't a new concept. Its roots go back to the 1960s with mainframes.

In those early days, the primary goal was to isolate workloads on a single powerful mainframe, allowing different applications to run without interfering with each other. As we moved into the 1990s, the challenge shifted to underutilized physical servers.

Organizations often had numerous dedicated servers, each running a single application, leading to significant waste of computing resources. This led to the emergence of virtualization as we know it today, primarily from the 1990s to the 2000s.

The core idea here was to run multiple isolated operating systems on a single physical server. This innovation dramatically improved the resource utilization and laid the technical foundation for cloud computing, enabling the scalable and flexible environments we rely on today.

02:39

Nikita: Interesting. So, from an economic standpoint, what pushed traditional data centers to change and opened the door to virtualization?

Orlando: In the past, running applications often meant running them on dedicated physical servers. This led to a few significant challenges.

First, more hardware purchases. Every new application, every new project often required its own dedicated server. This meant constantly buying new physical hardware, which quickly escalated capital expenditure.

Secondly, and hand-in-hand with more servers came higher power and cooling costs. Each physical server consumed power and generated heat, necessitating significant investment in electricity and cooling infrastructure. The more servers, the higher these operational expenses became.

And finally, a major problem was unused capacity. Despite investing heavily in these physical servers, it was common for them to run well below their full capacity. Applications typically didn't need 100% of server's resources all the time.

This meant we were wasting valuable compute power, memory, and storage, effectively wasting resources and diminishing the return of investment from those expensive hardware purchases. These economic pressures became a powerful incentive to find more efficient ways to utilize data center resources, setting the stage for technologies like virtualization.

04:18

Lois: I guess we can assume virtualization emerged as a financial game-changer. So, what kind of economic efficiencies did virtualization bring to the table?

Orlando: From a CapEx or capital expenditure perspective, companies spent less on servers and data center expansion. From an OpEx or operational expenditure perspective, fewer machines meant lower electricity, cooling, and maintenance costs.

It also sped up provisioning. Spinning a new VM took minutes, not days or weeks. That improved agility and reduced the operational workload on IT teams. It also created a more scalable, cost-efficient foundation which made virtualization not just a technical improvement, but a financial turning point for data centers.

This economic efficiency is exactly what cloud providers like Oracle Cloud Infrastructure are built on, using virtualization to deliver scalable pay as you go infrastructure.

05:22

Nikita: Ok, Orlando. Let's get into the core components of virtualization. To start, what exactly is a hypervisor?

Orlando: A hypervisor is a piece of software, firmware, or hardware that creates and runs virtual machines, also known as VMs.

Its core function is to allow multiple virtual machines to run concurrently on a single physical host server. It acts as virtualization layer, abstracting the physical hardware resources like CPU, memory, and storage, and allocating them to each virtual machine as needed, ensuring they can operate independently and securely.

06:02

Lois: And are there types of hypervisors?

Orlando: There are two primary types of hypervisors. The type 1 hypervisors, often called bare metal hypervisors, run directly on the host server's hardware.

This means they interact directly with the physical resources offering high performance and security. Examples include VMware ESXi, Oracle VM Server, and KVM on Linux. They are commonly used in enterprise data centers and cloud environments. In contrast, type 2 hypervisors, also known as hosted hypervisors, run on top of an existing operating system like Windows or macOS. They act as an application within that operating system. Popular examples include VirtualBox, VMware Workstation, and Parallels. These are typically used for personal computing or development purposes, where you might run multiple operating systems on your laptop or desktop.

07:08

Nikita: We've spoken about the foundation provided by hypervisors. So, can we now talk about the virtual entities they manage: virtual machines? What exactly is a virtual machine and what are its fundamental characteristics?

Orlando: A virtual machine is essentially a software-based virtual computer system that runs on a physical host computer. The magic happens with the hypervisor. The hypervisor's job is to create and manage these virtual environments, abstracting the physical hardware so that multiple VMs can share the same underlying resources without interfering with each other.

Each VM operates like a completely independent computer with its own operating system and applications.

07:53

Lois: What are the benefits of this?

Orlando: Each VM is isolated from the others. If one VM crashes or encounters an issue, it doesn't affect the other VMs running on the same physical host. This greatly enhances stability and security.

A powerful feature is the ability to run different operating systems side-by-side on the very same physical host. You could have a Windows VM, a Linux VM, and even other specialized OS, all operating simultaneously. Consolidate workloads directly addresses the unused capacity problem. Instead of one application per physical server, you can now run multiple workloads, each in its own VM on a single powerful physical server. This dramatically improves hardware utilization, reducing the need of constant new hardware purchases and lowering power and cooling costs.

And by consolidating workloads, virtualization makes it possible for cloud providers to dynamically create and manage vast pools of computing resources. This allows users to quickly provision and scale virtual servers on demand, tapping into these shared pools of CPU, memory, and storage as needed, rather than being tied to a single physical machine.

09:25

Do you want to boost your data management skills for free? The Oracle Data Platform Foundations Associate Learning Path covers everything from Autonomous Database to modern data architectures like lakehouse and mesh—and prepares you for the certification. Get started today by visiting mylearn.oracle.com.

09:50

Nikita: Welcome back! Orlando, let's move on to containers. Many see them as a lighter, more agile way to build and run applications. What's your take?

Orlando: A container packages an application in all its dependencies, like libraries and other binaries, into a single, lightweight executable unit. Unlike a VM, a container shares the host operating system's kernel, running on top of the container runtime process. This architectural difference provides several key advantages. Containers are incredibly portable. They can be taken virtually anywhere, from a developer's laptop to a cloud environment, and run consistently, eliminating it works on my machine issues. Because containers share the host OS kernel, they don't need to bundle a full operating system themselves. This results in significantly smaller footprints and less administration overhead compared to VMs.

They are faster to start. Without the need to boot a full operating system, containers can start up in seconds, or even milliseconds, providing rapid deployment and scaling capabilities.

11:08

Nikita: Ok. Throughout our conversation, you've spoken about the various advantages of virtualization but let's consolidate them now.

Orlando: From a security standpoint, virtualization offers several crucial benefits. Each VM operates in its own isolated sandbox. This means if one VM experiences a security breach, the impact is generally contained to that single virtual machine, significantly limiting the spread of potential threats across your infrastructure. Containers also provide some isolation.

Virtualization allows for rapid recovery. This is invaluable for disaster recovery or undoing changes after a security incident. You can implement separate firewalls, access rules, and network configuration for each VM. This granular control reduces the overall exposure and attack surface across your virtualized environments, making it harder for malicious actors to move laterally.

Beyond security, virtualization also brings significant advantages in terms of operational and agility benefits for IT management. Virtualization dramatically improves operational efficiency and agility. Things are faster. With virtualization, you can provision new servers or containers in minutes rather than days or weeks. This speed allows for quicker deployment of applications and services.

It becomes much simpler to deploy consistent environment using templates and preconfigured VM images or containers. This reduces errors and ensures uniformity across your infrastructure. It's more scalable. Virtualization makes your infrastructure far more scalable. You can reshape VMs and containers to meet changing demands, ensuring your resources align precisely with your needs.

These operational benefits directly contribute to the power of cloud computing, especially when we consider virtualization's role in enabling cloud and scalability. Virtualization is the very backbone of modern cloud computing, fundamentally enabling its scalability. It allows multiple virtual machines to run on a single physical server, maximizing hardware utilization, which is essential for cloud providers.

This capability is core of infrastructure as a service offerings, where users can provision virtualized compute resources on demand. Virtualization makes services globally scalable. Resources can be easily deployed and managed across different geographic regions to meet worldwide demand. Finally, it provides elasticity, meaning resources can be automatically scaled up or down in response to fluctuating workloads, ensuring optimal performance and cost efficiency.

14:18

Lois: That's amazing. Thank you, Orlando, for joining us once again.

Nikita: Yeah, and remember, if you want to learn more about the topics we covered today, go to mylearn.oracle.com and search for the Cloud Tech Jumpstart course.

Lois: Well, that's all we have for today. Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

14:37

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.

Have you ever wondered where all your digital memories, work projects, or favorite photos actually live in the cloud? In this episode, Lois Houston and Nikita Abraham discuss cloud storage. They explore how data is carefully organized, the different ways it can be stored—whether right next to the server or across the network—and what keeps it safe and easy to find. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------ Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:38

Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hey there! Last week, we spoke about the differences between traditional and cloud data centers, and covered components like CPU, RAM, and operating systems. If you haven't listened to the episode yet, I'd suggest going back and listening to it before you dive into this one.

Nikita: Joining us again is Orlando Gentil, Principal OCI Instructor at Oracle University, and we're going to ask him about another fundamental concept: storage.

01:16

Lois: That's right, Niki. Hi Orlando! Thanks for being with us again today. You introduced cloud data centers last week, but tell us, how is data stored and accessed in these centers?

Orlando: At a fundamental level, storage is where your data resides persistently. Data stored on a storage device is accessed by the CPU and, for specialized tasks, the GPU. The RAM acts as a high-speed intermediary, temporarily holding data that the CPU and the GPU are actively working on. This cyclical flow ensures that applications can effectively retrieve, process, and store information, forming the backbone for our computing operations in the data center.

02:05

Nikita: But how is data organized and controlled on disks?

Orlando: To effectively store and manage data on physical disks, a structured approach is required, which is defined by file systems and permissions. The process began with disks. These are the raw physical storage devices.

Before data can be written to them, disks are typically divided into partitions. A partition is a logical division of a physical disk that acts as if it were a separated physical disk. This allows you to organize your storage space and even install multiple operating systems on a single drive.

Once partitions are created, they are formatted with a file system.

02:53

Nikita: Ok, sorry but I have to stop you there. Can you explain what a file system is? And how is data organized using a file system?

Orlando: The file system is the method and the data structure that an operating system uses to organize and manage files on storage devices. It dictates how data is named, is stored, retrieved, and managed on the disk, essentially providing the roadmap for data. Common file systems include NTFS for Windows and ext4 or XFS for Linux.

Within this file system, data is organized hierarchically into directories, also known as folders. These containers help to logically group related files, which are the individual units of data, whether they are documents, images, videos, or applications. Finally, overseeing this entire organization are permissions.

03:55

Lois: And what are permissions?

Orlando: Permissions define who can access a specific files and directories and what actions they are allowed to perform-- for example, read, write, or execute.

This access control, often managed by user, group, and other permissions, is fundamental for security, data integrity, and multi-user environments within a data center.

04:21

Lois: Ok, now that we have a good understanding of how data is organized logically, can we talk about how data is stored locally within a server?

Orlando: Local storage refers to storage devices directly attached to a server or computer. The three common types are Hard Disk Drive. These are traditional storage devices using spinning platters to store data. They offer large capacity at a lower cost per gigabyte, making them suitable for bulk data storage when high performance isn't the top priority.

Unlike hard disks, solid state drives use flash memory to store data, similar to USB drives but on a larger scale. They provide significantly faster read and write speeds, better durability, and lower power consumption than hard disks, making them ideal for operating systems, applications, and frequently accessed data.

Non-Volatile Memory Express is a communication interface specifically designed for solid state that connects directly to the PCI Express bus. NVME offers even faster performance than traditional SATA-based solid state drives by reducing latency and increasing bandwidth, making it the top choice for demanding workloads that require extreme speed, such as high-performance databases and AI applications. Each type serves different performance and cost requirements within a data center. While local storage is essential for immediate access, data center also heavily rely on storage that isn't directly attached to a single server.

06:11

Lois: I'm guessing you're hinting at remote storage. Can you tell us more about that, Orlando?

Orlando: Remote storage refers to data storage solutions that are not physically connected to the server or client accessing them. Instead, they are accessed over the network. This setup allows multiple clients or servers to share access to the same storage resources, centralizing data management and improving data availability. This architecture is fundamental to cloud computing, enabling vast pools of shared storage that can be dynamically provisioned to various users and applications.

06:48

Lois: Let's talk about the common forms of remote storage. Can you run us through them?

Orlando: One of the most common and accessible forms of remote storage is Network Attached Storage or NAS. NAS is a dedicated file storage device connected to a network that allows multiple users and client devices to retrieve data from a centralized disk capacity. It's essentially a server dedicated to serving files.

A client connects to the NAS over the network. And the NAS then provides access to files and folders. NAS devices are ideal for scenarios requiring shared file access, such as document collaboration, centralized backups, or serving media files, making them very popular in both home and enterprise environments. While NAS provides file-level access over a network, some applications, especially those requiring high performance and direct block level access to storage, need a different approach.

07:50

Nikita: And what might this approach be?

Orlando: Internet Small Computer System Interface, which provides block-level storage over an IP network.

iSCSI or Internet Small Computer System Interface is a standard that allows the iSCSI protocol traditionally used for local storage to be sent over IP networks. Essentially, it enables servers to access storage devices as if they were directly attached even though they are located remotely on the network.

This means it can leverage standard ethernet infrastructure, making it a cost-effective solution for creating high performance, centralized storage accessible over an existing network. It's particularly useful for server virtualization and database environments where block-level access is preferred. While iSCSI provides block-level access over standard IP, for environments demanding even higher performance, lower latency, and greater dedicated throughput, a specialized network is often deployed.

08:59

Nikita: And what's this specialized network called?

Orlando: Storage Area Network or SAN. A Storage Area Network or SAN is a high-speed network specifically designed to provide block-level access to consolidated shared storage. Unlike NAS, which provides file level access, a SAN presents a storage volumes to servers as if they were local disks, allowing for very high performance for applications like databases and virtualized environments. While iSCSI SANs use ethernet, many high-performance SANs utilize fiber channel for even faster and more reliable data transfer, making them a cornerstone of enterprise data centers where performance and availability are paramount.

09:56

Do you want to master Oracle Database on AWS? Check out the Oracle Database@AWS course, where you'll learn provisioning, migration, security, and high availability. Validate your new skills with a certification and stand out in the multicloud space. Visit mylearn.com to learn more!

10:23

Nikita: Welcome back! Orlando, are there any other popular storage paradigms we should know about?

Orlando: Beyond file level and block level storage, cloud environments have popularized another flexible and highly scalable storage paradigm, object storage.

Object storage is a modern approach to storing data, treating each piece of data as a distinct, self-contained unit called an object. Unlike file systems that organize data in a hierarchy or block storage that breaks data into fixed size blocks, object storage manages data as flat, unstructured objects. Each object is stored with unique identifiers and rich metadata, making it highly scalable and flexible for massive amounts of data.

This service handles the complexity of storage, providing access to vast repositories of data. Object storage is ideal for use cases like cloud-native applications, big data analytics, content distribution, and large-scale backups thanks to its immense scalability, durability, and cost effectiveness. While object storage is excellent for frequently accessed data in rapidly growing data sets, sometimes data needs to be retained for very long periods but is accessed infrequently. For these scenarios, a specialized low-cost storage tier, known as archive storage, comes into play.

11:59

Lois: And what's that exactly?

Orlando: Archive storage is specifically designed for long-term backup and retention of data that you rarely, if ever, access. This includes critical information, like old records, compliance data that needs to be kept for regulatory reasons, or disaster recovery backups. The key characteristics of archive storage are extremely low cost per gigabyte, achieved by optimizing for infrequent access rather than speed. Historically, tape backup systems were the common solution for archiving, where data from a data center is moved to tape. In modern cloud environments, this has evolved into cloud backup solutions. Cloud-based archiving leverages high-cost, effective during cloud storage tiers that are purpose built for long term retention, providing a scalable and often more reliable alternative to physical tapes.

13:01

Lois: Thank you, Orlando, for taking the time to talk to us about the hardware and software layers of cloud data centers. This information will surely help our listeners to make informed decisions about cloud infrastructure to meet their workload needs in terms of performance, scalability, cost, and management.

Nikita: That's right, Lois. And if you want to learn more about what we discussed today, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course.

Lois: In our next episode, we'll take a look at more of the fundamental concepts within modern cloud environments, such as Hypervisors, Virtualization, and more. I can't wait to learn more about it. Until then, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

13:44

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.

Curious about what really goes on inside a cloud data center? In this episode, Lois Houston and Nikita Abraham dive into how cloud data centers are transforming the way organizations manage technology. They explore the differences between traditional and cloud data centers, the roles of CPUs, GPUs, and RAM, and why operating systems and remote access matter more than ever. Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript:

00:00

Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive.

00:12

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!

00:37

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, Team Lead: Editorial Services.

Nikita: Hi everyone! Today, we're covering the fundamentals you need to be successful in a cloud environment. If you're new to cloud, coming from a SaaS environment, or planning to move from on-premises to the cloud, you won't want to miss this. With us today is Orlando Gentil, Principal OCI Instructor at Oracle University. Hi Orlando! Thanks for joining us.

01:13

Lois: So Orlando, we know that Oracle has been a pioneer of cloud technologies and has been pivotal in shaping modern cloud data centers, which are different from traditional data centers. For our listeners who might be new to this, could you tell us what a traditional data center is?

Orlando: A traditional data center is a physical facility that houses an organization's mission critical IT infrastructure, including servers, storage systems, and networking equipment, all managed on site.

01:44

Nikita: So why would anyone want to use a cloud data center?

Orlando: The traditional model requires significant upfront investment in physical hardware, which you are then responsible for maintaining along with the underlying infrastructure like physical security, HVAC, backup power, and communication links.

In contrast, cloud data centers offer a more agile approach. You essentially rent the infrastructure you need, paying only for what you use. In the traditional data center, scaling resources up and down can be a slow and complex process.

On cloud data centers, scaling is automated and elastic, allowing resources to adjust dynamically based on demand. This shift allows business to move their focus from the constant upkeep of infrastructure to innovation and growth.

The move represents a shift from maintenance to momentum, enabling optimized costs and efficient scaling. This fundamental shift is how IT infrastructure is managed and consumed, and precisely what we mean by moving to the cloud.

02:52

Lois: So, when we talk about moving to the cloud, what does it really mean for businesses today?

Orlando: Moving to the cloud represents the strategic transition from managing your own on-premise hardware and software to leveraging internet-based computing services provided by a third-party.

This involves migrating your applications, data, and IT operations to a cloud environment. This transition typically aims to reduce operational overhead, increase flexibility, and enhance scalability, allowing organizations to focus more on their core business functions.

03:29

Nikita: Orlando, what's the "brain" behind all this technology?

Orlando: A CPU or Central Processing Unit is the primary component that performs most of the processing inside the computer or server. It performs calculations handling the complex mathematics and logic that drive all applications and software.

It processes instructions, running tasks, and operations in the background that are essential for any application. A CPU is critical for performance, as it directly impacts the overall speed and efficiency of the data center.

It also manages system activities, coordinating user input, various application tasks, and the flow of data throughout the system. Ultimately, the CPU drives data center workloads from basic server operations to powering cutting edge AI applications.

04:23

Lois: To better understand how a CPU achieves these functions and processes information so efficiently, I think it's important for us to grasp its fundamental architecture. Can you briefly explain the fundamental architecture of a CPU, Orlando?

Orlando: When discussing CPUs, you will often hear about sockets, cores, and threads. A socket refers to the physical connection on the motherboard where a CPU chip is installed.

A single server motherboard can have one or more sockets, each holding a CPU. A core is an independent processing unit within a CPU. Modern CPUs often have multiple cores, enabling them to handle several instructions simultaneously, thus increasing processing power.

Think of it as having multiple mini CPUs on a single chip. Threads are virtual components that allow a single CPU core to handle multiple sequence of instructions or threads concurrently. This technology, often called hyperthreading, makes a single core appear as two logical processors to the operating system, further enhancing efficiency.

05:39

Lois: Ok. And how do CPUs process commands?

Orlando: Beyond these internal components, CPUs are also designed based on different instruction set architectures which dictate how they process commands.

CPU architectures are primarily categorized in two designs-- Complex Instruction Set Computer or CISC and Reduced Instruction Set Computer or RISC. CISC processors are designed to execute complex instructions in a single step, which can reduce the number of instructions needed for a task, but often leads to a higher power consumption. These are commonly found in traditional Intel and AMD CPUs.

In contrast, RISC processors use a simpler, more streamlined set of instructions. While this might require more steps for a complex task, each step is faster and more energy efficient. This architecture is prevalent in ARM-based CPUs.

06:47

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Nikita: Welcome back! We were discussing CISC and RISC processors. So Orlando, where are they typically deployed? Are there any specific computing environments and use cases where they excel?

Orlando: On the CISC side, you will find them powering enterprise virtualization and server workloads, such as bare metal hypervisors in large databases where complex instructions can be efficiently processed. High performance computing that includes demanding simulations, intricate analysis, and many traditional machine learning systems.

Enterprise software suites and business applications like ERP, CRM, and other complex enterprise systems that benefit from fewer steps per instruction. Conversely, RISC architectures are often preferred for cloud-native workloads such as Kubernetes clusters, where simpler, faster instructions and energy efficiency are paramount for distributed computing.

Mobile device management and edge computing, including cell phones and IoT devices where power efficiency and compact design are critical. Cost optimized cloud hosting supporting distributed workloads where the cumulative energy savings and simpler design lead to more economical operations.

The choice between CISC and RISC depends heavily on the specific workload and performance requirements. While CPUs are versatile generalists, handling a broad range of tasks, modern data centers also heavily rely on another crucial processing unit for specialized workloads.

09:07

Lois: We've spoken a lot about CPUs, but our conversation would be incomplete without understanding what a Graphics Processing Unit is and why it's important. What can you tell us about GPUs, Orlando?

Orlando: A GPU or Graphics Processing Unit is distinct from a CPU. While the CPU is a generalist excelling at sequential processing and managing a wide variety of tasks, the GPU is a specialist.

It is designed specifically for parallel compute heavy tasks. This means it can perform many calculations simultaneously, making it incredibly efficient for workloads like rendering graphics, scientific simulations, and especially in areas like machine learning and artificial intelligence, where massive parallel computation is required.

In the modern data center, GPUs are increasingly vital for accelerating these specialized, data intensive workloads.

10:11

Nikita: Besides the CPU and GPU, there's another key component that collaborates with these processors to facilitate efficient data access. What role does Random Access Memory play in all of this?

Orlando: The core function of RAM is to provide faster access to information in use. Imagine your computer or server needing to retrieve data from a long-term storage device, like a hard drive. This process can be relatively slow.

RAM acts as a temporary high-speed buffer. When your CPU or GPU needs data, it first checks RAM. If the data is there, it can be accessed almost instantaneously, significantly speeding up operations.

This rapid access to frequently used data and programming instructions is what allows applications to run smoothly and systems to respond quickly, making RAM a critical factor in overall data center performance.

While RAM provides quick access to active data, it's volatile, meaning data is lost when power is off, or persistent data storage, the information that needs to remain available even after a system shut down.

11:26

Nikita: Let's now talk about operating systems in cloud data centers and how they help everything run smoothly. Orlando, can you give us a quick refresher on what an operating system is, and why it is important for computing devices?

Orlando: At its core, an operating system, or OS, is the fundamental software that manages all the hardware and software resources on a computer. Think of it as a central nervous system that allows everything else to function.

It performs several critical tasks, including managing memory, deciding which programs get access to memory and when, managing processes, allocating CPU time to different tasks and applications, managing files, organizing data on storage devices, handling input and output, facilitate communication between the computer and its peripherals, like keyboards, mice, and displays. And perhaps, most importantly, it provides the user interface that allows us to interact with the computer.

12:31

Lois: Can you give us a few examples of common operating systems?

Orlando: Common operating system examples you are likely familiar with include Microsoft Windows and MacOS for personal computers, iOS and Android for mobile devices, and various distributions of Linux, which are incredibly prevalent in servers and increasingly in cloud environments.

12:54

Lois: And how are these operating systems specifically utilized within the demanding environment of cloud data centers?

Orlando: The two dominant operating systems in data centers are Linux and Windows. Linux is further categorized into enterprise distributions, such as Oracle Linux or SUSE Linux Enterprise Server, which offer commercial support and stability, and community distributions, like Ubuntu and CentOS, which are developed and maintained by communities and are often free to use.

On the other side, we have Windows, primarily represented by Windows Server, which is Microsoft's server operating system known for its robust features and integration with other Microsoft products. While both Linux and Windows are powerful operating systems, their licensing modes can differ significantly, which is a crucial factor to consider when deploying them in a data center environment.

13:55

Nikita: In what way do the licensing models differ?

Orlando: When we talk about licensing, the differences between Linux and Windows become quite apparent. For Linux, Enterprise Distributions come with associated support fees, which can be bundled into the initial cost or priced separately. These fees provide access to professional support and updates. On the other hand, Community Distributions are typically free of charge, with some providers offering basic community-driven support.

Windows server, in contrast, is a commercial product. Its license cost is generally included in the instance cost when using cloud providers or purchased directly for on-premise deployments. It's also worth noting that some cloud providers offer a bring your own license, or BYOL program, allowing organizations to use their existing Windows licenses in the cloud, which can sometimes provide cost efficiencies.

14:58

Nikita: Beyond choosing an operating system, are there any other important aspects of data center management?

Orlando: Another critical aspect of data center management is how you remotely access and interact with your servers. Remote access is fundamental for managing servers in a data center, as you are rarely physically sitting in front of them. The two primary methods that we use are SSH, or secure shell, and RDP, remote desktop.

Secure shell is widely used for secure command line access for Linux servers. It provides an encrypted connection, allowing you to execute commands, transfer files, and manage your servers securely from a remote location. The remote desktop protocol is predominantly used for graphical remote access to Windows servers. RDP allows you to see and interact with the server's desktop interface, just as if you were sitting directly in front of it, making it ideal for tasks that require a graphical user interface.

16:06

Lois: Thank you so much, Orlando, for shedding light on this topic.

Nikita: Yeah, that's a wrap for today! To learn more about what we discussed, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. In our next episode, we'll take a close look at how data is stored and managed. Until then, this is Nikita Abraham…

Lois: And Lois Houston, signing off!

16:28

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.

Hosts Lois Houston and Nikita Abraham are joined by Brent Dayley, Senior Principal APEX and Apps Dev Instructor, to explore the latest vector AI supporting features in Oracle Exadata and GoldenGate 23ai. The conversation begins with an overview of Exadata's capabilities and then shifts to how GoldenGate is powering distributed AI, real-time data streaming, and analytics with advanced microservices architecture. Brent highlights recent GoldenGate enhancements, including distributed vector support, robust monitoring, OCI IAM integration, and support for next-generation AI workloads via real-time vector hubs. Oracle AI Vector Search Deep Dive: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-deep-dive/144706/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, and the OU Studio Team for helping us create this episode. Please note, this episode was recorded before Oracle AI Database 26ai replaced Oracle Database 23ai. However, all concepts and features discussed remain fully relevant to the latest release. ------------------------------------------------------- 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 another episode of the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead of Editorial Services with Oracle University.

Nikita: Hi everyone! Thanks for joining us! In our previous episode of this series, we took a deep dive into Oracle AI Vector Search and Retrieval Augmented Generation, or RAG, showing how unstructured data can be transformed into embeddings to power smarter, more context-aware AI with Oracle Database 23ai.

Lois: That's right, Niki. We also explored how the OCI Generative AI service can be used with both Python and PL/SQL, and how AI Vector Search enables relevant information retrieval for large language model prompts.

01:21

Nikita: Today, we're focusing on the latest supporting features for Oracle AI Vector Search. Joining us once again is Brent Dayley, Senior Principal APEX and Apps Dev Instructor. Welcome back, Brent! To kick things off, could you outline what's new in Exadata with the 24ai release, particularly for AI storage?

Brent: So Exadata has ushered in a new era of AI capabilities with 24ai release. Key features of Exadata system software 24ai include AI Smart Scan, Exadata RDMA Memory, known as XRMEM, Exadata Smart Flash Cache, and on-storage processing.

In-Memory Columnar Speed JSON Queries, Transparent Cross-Tier Scans, and caching enhancements, including Columnar Smart Scan at Memory Speed, Exadata Cache Observability, and Automatic KEEP Object Load into Exadata Flash Cache.

Now, Exadata system software 24ai is a significant release. It ushers in a new era of AI capabilities for Oracle Database users.

Now there have been some infrastructure improvements, including the ability to increase the number of virtual machines on X10M and Secure Boot for KVM Virtual Machines.

We have also improved and enhanced high availability and network resilience, including improved RoCE Network Resilience and enhanced RoCE Network Discovery. There have been some enhancements for monitoring and management, including AWR and SQL Monitor Enhancements and JSON API for Management Server.

Additionally, security enhancement. SNMP Security. Now, Exadata system software 24ai is supported on Exadata database machines and storage expansion racks from X6 and newer.

03:40

Lois: Those are some fantastic advancements for Exadata users. Now, let's pivot to distributed AI. Brent, can you walk us through how GoldenGate enables distributed AI?

Brent: Let's take a look at some common GoldenGate use cases as a refresher. The first use case is multi-active, high availability, and cross-region deployments, spanning on-premises and cloud environments.

Another use case includes data offloading and data hub creation in order to support multiple downstream applications. Real-time data stores for Downstream Marts and Analytics. Micro and mini services architecture and an audit history of transactions.

Other use cases include migrations and upgrades of databases, including OCI-hosted databases. Another use case would be creating analytic data feeds for various applications, including SaaS and on-premises apps. And finally, stream analytics using application and transaction events captured by GoldenGate Stream Analytics.

05:03

Nikita: We know GoldenGate has long been a staple for enterprise data integration. So Brent, what makes GoldenGate the best choice today, and how has its architecture evolved?

Brent: It offers DIY Stream Analytics. GoldenGate does remain the top choice for Enterprise Standard, real-time data streaming. It supports Oracle and third-party databases, vector sources, messaging systems, and NoSQL databases.

OCI offers a fully managed pipeline builder for Stream Analytics. This pipeline leverages various OCI services, such as OCI Streaming for real-time event ingestion, OCI Dataflow for stream processing, OCI Big Data for data storage and processing, and OCI Stream Analytics for real-time event processing and analysis.

GoldenGate microservices, available since 2017 in Oracle GoldenGate 12.3, is used in over 4,000 deployments in OCI. Benefits of GoldenGate microservices include the ability to employ the same trusted Extract and Replicat processes as the classic architecture.

Provides flexible and secure remote administration through a user-friendly web interface or CLI. Deployable on-premises in OCI as a service and in third-party cloud environments. Simplified patching and upgrading process.

Now the GoldenGate architecture evolution. First, classic architecture that was deprecated in version 19c and desupported in 23ai. Microservices Architecture introduced in version 12.3 and is the recommended architecture. A migration utility is available to upgrade from classic to microservices architecture.

07:12

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07:39

Nikita: Welcome back! It sounds like the latest GoldenGate updates offer new features and integrations. Could you share more about these enhancements?

Brent: There are many new features and enhancements in GoldenGate, along with microservices, including a redesigned GUI for enhanced usability. Integration with StatsD and Telegraf for monitoring and metrics. OCI IAM integration for secure access control. JSON Relational Duality for flexible data handling. Next-generation AI with distributed vector support. PDB Extract Capture for efficient data extraction from Oracle Pluggable Databases. DDL notification on Target Tables for schema evolution management.

Support for non-Oracle and Big Data technologies. Online DDL and EBR enhancement for improved performance. Data Streams Pub-Sub for asynchronous data dissemination. Async API support for standardized event communication. High-availability clusters for increased resilience. Trail Files Management for efficient data storage. And support for new features in 23ai database.

It also includes integrated diagnostics for improved troubleshooting of IE and IR processes. And 30 or more OS and database certifications for wider platform support. @Dbfunction Mapping for custom data transformations. And lastly, GoldenGate free recipes for pre-built solutions and best practices.

New in GoldenGate, distributed AI processing with vector replication.

09:37

Lois: And what type of use cases does this enable?

Brent: Migrating vectors into Oracle Vector Database. Replicating and consolidating vector changes. Implementing multi-cloud, multi-active Oracle vector databases. Streaming text and vector changes to search engines.

Key considerations include that embedding models must be consistent across all vector stores for effective similarity searches.

10:09

Lois: Now, many organizations wonder if they can use generative AI with their own business data. Brent, how do enterprises typically approach this?

Brent: Organizations are using generative AI typically like this.

Building LLMs from scratch. Training models on proprietary data for specific tasks. Fine-tuning LLMs, adapting pre-trained models to a specific domain using private data. And prompt engineering with retrieval augmented generation or RAG. Augmenting prompts with relevant information retrieved from a knowledge base to improve the accuracy and relevance of LLM responses.

Now it's possible to create a real-time vector hub for GenAI. This hub can ingest real-time data from various sources, including Oracle and third-party relational databases, vector databases, third-party messaging systems, and NoSQL databases, business updates, documents, events, and alerts.

11:11

Nikita: And how does the vector hub work?

Brent: DML and DDL changes, vector changes, and prompt or chat history are used to enrich prompts. And embedding model generates embeddings from the text data.

Similarity search is performed on these embeddings to retrieve relevant information from the vector hub. The retrieved information is used to augment the prompt, leading to more accurate and trustworthy answers from the LLM. Now, the benefits of real-time data and generative AI include the ability to ensure answers are based on fresh business data. And helps reduce hallucinations in generative AI responses.

Actionable AI and machine learning from streaming pipelines allows data from ERP and SaaS applications, databases, event messaging systems, and NoSQL databases to be ingested into streaming pipelines. This data can then be used for AI and machine learning model training, similarity searches, machine learning tasks, external AI, and machine learning integrations, alerts, and data product creation.

12:25

Lois: So if you had to summarize, Brent, why does GoldenGate 23ai stand out for artificial intelligence workloads?

Brent: Well, first up, it improves data quality for AI model training and fine-tuning. And secondly, it enhances retrieval augmented generation by providing real-time access to relevant business data, leading to more accurate and trustworthy generative AI responses.

Nikita: Thank you, Brent, for sharing your insights and detailing these exciting new features across Oracle's AI stack. If you'd like to dive deeper into these topics, don't forget to visit mylearn.oracle.com and look for Oracle AI Vector Search Deep Dive course. Until next time, this is Nikita Abraham…

Lois: And Lois Houston, signing off!

13:16

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 of the Oracle University Podcast, hosts Lois Houston and Nikita Abraham are joined by Brent Dayley, Senior Principal APEX & Apps Dev Instructor. Together, they explore how to implement Retrieval Augmented Generation (RAG) using Oracle AI Vector Search and OCI Generative AI. Brent walks listeners through the similarities and differences between building RAG workflows with Python and PL/SQL, offering practical insights into embedding creation, semantic search, and prompt engineering within Oracle's technology stack. Oracle AI Vector Search Deep Dive: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-deep-dive/144706/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode. Please note, this episode was recorded before Oracle AI Database 26ai replaced Oracle Database 23ai. However, all concepts and features discussed remain fully relevant to the latest release. -------------------------------------------- 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 another episode of the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead for Editorial Services with Oracle University.

Nikita: Hi everyone! If you joined us last week, you'll remember we explored AI Vector Search and how Retrieval Augmented Generation, or RAG, empowers large language models by surfacing relevant business content for smarter, more context-aware answers.

Lois: That's right, Niki. We also looked at how unstructured data gets transformed into embeddings, how these vectors power semantic search, and how Oracle Database 23ai is uniquely designed to support these advanced AI workflows.

Nikita: Today, we're building on that foundation with an exciting double feature. We'll start with an introduction to OCI Generative AI Service and how you can use it with Python, and then dive into Retrieval Augmented Generation with Oracle AI Vector Search and the OCI Gen AI service using PL/SQL.

01:32

Lois: And to walk us through these topics, we're delighted to welcome back Brent Dayley, Senior Principal APEX & Apps Dev Instructor. Brent, it's great to have you. So, tell us, how does the OCI Generative AI service use Oracle AI Vector Search?

Brent: So OCI Generative AI service allows us to take user questions and augment those using external data from outside of the large language model that allows us to return augmented content.

We would leverage Oracle AI Vector Search in order to retrieve contextually relevant information. And we would create prompts that have some sort of a meaning to help guide the user to input the appropriate types of questions. And this allows us to retrieve the data using a large language model.

02:27

Nikita: What are the typical steps for implementing a RAG workflow using the OCI Generative AI service in Python?

Brent: We would load the document. Transform the document to text. And then split the text into chunks.

So if you're talking about maybe a PDF that contains chapters, we might split the different chapters into individual chunks. We would then set up Oracle AI Vector Search and insert the embedding vectors. We would build the prompt to query the document. And then we would invoke the chain.

So first, you would load the text sources from a file. Open a terminal window and connect to your compute instance. And launch ipython to allow interactive work.

Ipython allows you to insert a series of steps in order to put different commands in different steps. You might load the source file called FAQs.

Next, you would load the FAQ chunks into the Vector Database. You would create a connection and connect to your database. And then create the table. And then you would vectorize the text chunks and then encode the text chunks. And then insert the chunks and vectors into the database.

Next, you would vectorize the question. Define the SQL script ordering the results by the calculated score. Define the question. Write the retrieval code. And then execute the code. Finally, you would print the results.

Then we would create the large language model prompt and call the AI generative LLM. Ensure that our prompt does not exceed the maximum context length of the model. And then define the prompt content.

We would then initialize the OCI client and then make the call.

04:47

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05:12

Nikita: Thanks, Brent. That gives us a nice overview of how Python can be leveraged with OCI Generative AI. Now, how would you compare working with Python for building RAG applications to using PL/SQL? Can you walk us through the high-level process for building a RAG solution in this environment?

Brent: First, we would want to load the document. Next, we would transform the document into plain text. After that, we would take that text and split it into meaningful chunks. Next, we would go ahead and set up Oracle AI Vector Search and insert the embedding vectors. We would then build the prompt so that we can query the document. And then we would invoke all of those previous steps as our chain.

06:04

Lois: OK, and can we take a closer look at each of these steps?

Brent: Step 1, text extraction and preparation. So, let's imagine we have some sort of document that we want to use as the augmented information. We would load that document. Next, we would transform the document to text. And we have a function in the DBMS Vector Chain Package called util to text. And this is used to extract plain text from the loaded documents.

Next, we would want to split the text into meaningful chunks. The DBMS Vector Chain Package has another function called util two chunks, that allows us to divide the extracted text into smaller, more manageable pieces, which we call chunks.

07:02

Nikita: Once we have our text chunks ready, what's the next step to make our data searchable and useful for the large language model?

Brent: Step number 2, we would want to go ahead and use embedding models in order to create our vectors. We would load multiple ONNX models into the database. And the reason we would do this is because models with a greater number of dimensions usually produce higher quality vector embeddings.

So you might want to load multiple different ONNX models into the database so that you can generate embeddings from each of the models, and then compare those vector embeddings using those different models. You would create vector embeddings using PL/SQL packages.

07:55

Lois: After embeddings are created, how does the solution find the most relevant content in response to a user's question?

Brent: Step 3, we would then go and do a similarity search so that we can return a response. We would select the text chunks that have the relevant information for the input user question based on vector search. This allows for integrating with Oracle's Gen AI Large Language Model Service to generate responses. The process ensures that the large language model generates contextually appropriate and relevant answers for those users' queries.

Now, step 4 is to build the prompt, and I want to stress the importance of large language model prompt engineering. What this will do is to carefully craft input queries or instructions so that we can get more accurate and desirable outputs from the large language model.

This allows developers to guide the LLM's behavior and tailor its responses to specific requirements. This is what we call LLM Prompt Engineering. And it allows us, as I was saying, to craft input queries or instructions so that we can create more accurate and desirable outputs.

Next, we would use an example interactive RAG application that uses the Streamlit framework in order to create a user-friendly interface. This interface will allow us to upload documents, pose the question, and receive relevant answers generated by the underlying RAG pipeline within the database.

In the final step, we will have an input prompt that asks us to ask a question about the PDF. We will then type in some sort of a question relative to the PDF content. And then we would retrieve the return data based on the input question.

10:11

Nikita: Brent, thank you for walking us through both the Python and PL/SQL approaches for building RAG solutions with Oracle Generative AI. If you'd like to dive deeper into these topics, don't forget to visit mylearn.oracle.com and look for the Oracle AI Vector Search Deep Dive course. Until next time, this is Nikita Abraham…

Lois: And Lois Houston, signing off!

10:33

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.

Join hosts Lois Houston and Nikita Abraham as they explore one of the most exciting innovations in enterprise AI: Retrieval Augmented Generation (RAG) powered by Oracle AI Vector Search. In this episode, Senior Principal APEX & Apps Dev Instructor Brent Dayley walks through the fundamentals of RAG, explaining how it combines Oracle Database 23ai, vector embeddings, and large language models to deliver accurate, context-rich answers from both business and unstructured data. Discover the typical RAG workflow, practical setup steps on Oracle Cloud Infrastructure, and how to work with embedding models for real-world applications. Oracle AI Vector Search Deep Dive: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-deep-dive/144706/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode. Please note, this episode was recorded before Oracle AI Database 26ai replaced Oracle Database 23ai. However, all concepts and features discussed remain fully relevant to the latest release. ---------------------------------------------- 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, Team Lead: Editorial Services with Oracle University, and joining me is Lois Houston, Director of Communications and Adoption Programs with Customer Success Services.

Lois: Hi everyone! If you've been with us this season, you'll know we've already covered a lot about Oracle AI Vector Search. In Episode 1, we introduced the core concepts—how vectors let you search by meaning, not just keywords, and how embedding models translate your unstructured data into a searchable format inside Oracle Database 23ai.

Nikita: Then, in Episode 2, we took a deeper dive into how these vectors are actually stored and managed. We explored the different types of vector indexes, similarity metrics, and best practices for designing and optimizing your database for semantic search.

Lois: Right. Today, we're shifting gears into one of the most exciting real-world applications: Retrieval Augmented Generation, or RAG. You'll learn how RAG combines the power of Oracle AI Vector Search with large language models to answer natural language questions using both business and unstructured data.

01:39

Nikita: We'll walk through the workflow, highlight why Oracle Database is uniquely suited for RAG, and give you the essential steps to get started. Back again is Senior Principal APEX & Apps Dev Instructor Brent Dayley. Hi Brent! Could you explain what RAG is, and why it's important for working with AI and large language models?

Brent: Well, RAG stands for Retrieval Augmented Generation. And this is a technique that allows us to enhance the capabilities of large language models, also known as LLMs, and this provides them with relevant context from external knowledge sources. This will allow the LLMs to generate more accurate, informative, and context-aware responses. Real world applications include answering questions, chatbot development, content summarization, and knowledge discovery.

02:35

Lois: Brent, what makes Oracle Database 23ai a good platform for implementing RAG workflows?

Brent: Now, there are some key advantages of using Oracle Database 23ai as a RAG platform. These include native functionality, allowing built-in tools and packages specifically designed for RAG pipeline development.

Also, if you are a PL/SQL developer, then this will allow you to develop within a familiar and robust database environment. Also, Oracle has a plethora of security and performance tools. And this ensures enhanced security and optimized performance.

03:18

Nikita: What does a typical RAG workflow look like in Oracle Database 23ai? What are the main steps involved?

Brent: Now, the primary workflow steps are going to be to generate vector embeddings from your unstructured data. You do this using vector embedding models. And you can generate those embeddings either inside or outside of the database.

Next, you need to store the vector embeddings, the unstructured data, and the relational business data, and you can store all of that in the Oracle Database. You might want to also create vector indexes that can allow you to run similarity searches over huge vector spaces with really good performance.

Finally, you need to query data with similarity searches. You can use Oracle AI Vector Search native SQL operations to combine similarity with relational searches to retrieve relevant data. And optionally, you can generate a prompt and send it to a large language model for full RAG inference.

04:30

Lois: Can you give us an example of how this workflow operates in practice?

Brent: A user's natural language question is encoded as a vector and sent to AI Vector Search. Next, AI vector search finds private content, such as documents, that are stored in the database, and those will match the user's question. The content is then sent to Oracle's GenAI service to help answer the user's question. And then GenAI uses the content plus general knowledge to provide an informed answer back to the user.

05:14

Nikita: What does the overall user experience look like when interacting with RAG? How does Oracle ensure the answers are both accurate and up to date?

Brent: In this case, we have a chatbot. This is the interface that we usually use to enable dialogue with the large language model. Now, in order to improve the quality of the answers, we want to search your private business data, and that allows us to pass the most relevant facts back to the LLM.

Next, we want to format the similarity search results as a prompt and context for the large language model. Now, this will allow us to use up to date facts as input to LLMs. And that will minimize the probability of the LLM hallucinating. And those high-quality responses are then returned back to the chatbot.

06:12

Lois: Brent, what does the setup process look like for getting RAG up and running with Oracle AI Vector Search on OCI? Can you take us through the main steps?

Brent: First, you will log into OCI. Provide your cloud account name and click Next. There are also interfaces for signing in using a traditional cloud account. And if you're not an Oracle Cloud customer yet, you can also sign up using this page.

Next, after signing in, you will create a compute instance. And you will use Oracle Infrastructure Cloud Console in order to do this. And you will wind up with the user called OPC. You'll notice that you're using SSH in order to connect to your compute instance, and you're running a script in order to set up the Oracle Database.

After that, you will set up the Python environment, again using SSH to connect as an OPC user to your compute instance.

07:22

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07:43

Nikita: Welcome back! So far, we've seen how Oracle AI Vector Search powers RAG, letting you surface relevant business knowledge for large language models and enhance their answers. At the heart of all this is the process of transforming unstructured data, like text or documents, into mathematical representations called embeddings.

Lois: Those embeddings are what make meaningful, semantic search possible. But have you wondered how those embeddings actually get created, or what goes on behind the scenes when you choose an embedding model?

Nikita: Up next, we'll take a closer look at embedding models themselves: what they are, how to use them inside Oracle Database 23ai, and how you can experiment with different models to get the results that best fit your business needs.

Lois: We'll walk through importing models, generating embeddings, and even how you can swap out embedding models to compare results. But before we get into the nitty-gritty details, let's quickly recap embedding models, since we've mentioned them in our previous episodes.

08:47

Nikita: Brent, for listeners who might need a refresher, can you explain what embedding models are and why they're so central to AI Vector Search?

Brent: AI Vector Search is based on similarity properties. You can search data by semantic similarity rather than by the actual values. Vector embeddings are created by embedding models to represent the unstructured data. So we have input data.

What we'll want to do is to use an embedding model to generate vector embeddings. And then the vector embeddings would be stored inside of a vector column in a table. We would then compare those vectors to each other using vector distance function.

And we would get the relevant content back based on the number of returns that we describe. For instance, maybe we want to bring back the five closest pieces of data compared to the input data.

There is a new function that allows you to generate vector embeddings that is called the vector embedding function. It allows you to generate vectors within the database.

10:08

Lois: Can you walk us through the practical steps for using embedding models with Oracle AI Vector Search?

Brent: In order to create and set up a table, we might use the Python program called create_schema.py. And that will allow us to create a table.

We would ensure that the table was successfully created with the data. As an example, I would create a table called MY_DATA. Next, we would use a sentence transformers embedding model in order to vectorize the table. We can use the Python program, vectorize_table_SentenceTransformers.py. We would then query the MY_DATA table in the Oracle Database to verify that the data has been updated.

And then we would use sentence transformers in order to perform the similarity search. The Python program is called similarity_search_SentenceTransformers.py And what that would do is create the table and then perform a similarity search using the sentence transformers. Now what if you decide that you want to maybe change embedding models? Maybe you want to compare the results by using one particular model as compared to a different model.

So you can change the embedding model. And in order to do that, you would change the embedding model in both of the programs and re-vectorize the table using the vectorize_table_SentenceTransformers.py program. You would then use the new model with different words, possibly, and then compare and review the results, and then choose which one gets you back the data that you're looking for that is most similar.

12:02

Nikita: Well, that's a wrap on this episode. A big thank you, Brent, for sharing your expertise with us.

Lois: If you want to learn more about the topics we discussed today, visit to mylearn.oracle.com and search for the Oracle AI Vector Search Deep Dive course. Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

12:25

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.

Go deeper into Oracle AI Vector Search as hosts Lois Houston and Nikita Abraham, along with Senior Principal APEX & Apps Dev Instructor Brent Dayley, break down how vector indexes, memory requirements, and similarity metrics make fast, powerful semantic search possible in Oracle Database 23ai. Learn about the different types of vector indexes, the VECTOR data type, and how exact and approximate similarity searches work, including best practices for vector management and search performance. Oracle AI Vector Search Fundamentals: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-fundamentals/140188/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode. *Please note, this episode was recorded before Oracle AI Database 26ai replaced Oracle Database 23ai. However, all concepts and features discussed remain fully relevant to the latest release. ---------------------------------------- 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, Team Lead: Editorial Services with Oracle University, and joining me is Lois Houston, Director of Communications and Adoption Programs with Customer Success Services.

Lois: Hi everyone! Thanks for joining us again as we continue our exploration into the exciting world of Oracle AI Vector Search. In today's episode, we're taking you inside the technology powering vector search in Oracle Database 23ai. We'll break down core concepts like vector indices, how vectors are stored and managed, and how you can use similarity metrics to unlock new possibilities with your data.

01:09

Nikita: We'll also dig into best practices for handling vectors, everything from memory requirements and table creation to the nuts and bolts of running both exact and approximate similarity searches. Back with us today is Senior Principal APEX & Apps Dev Instructor Brent Dayley. Hi Brent! What exactly are vector indexes?

Brent: Now, vector indexes are specialized indexing data structures that can make your queries more efficient against your vectors. They use techniques such as clustering, and partitioning, and neighbor graphs. Now, they greatly reduce the search space, which means that your queries happen quicker. They're also extremely efficient. They do require that you enable the vector pool in the SGA.

02:06

Lois: And are there different types of vector indices supported?

Brent: So, Oracle AI Vector Search supports two types of indexes, in-memory neighbor graph vector index. HNSW is the only type of in-memory neighbor graph vector index that is supported. These are very efficient indexes for vector approximate similarity search. HNSW graphs are structured using principles from small world networks along with layered hierarchical organization.

And neighbor partition vector index. Neighbor partition vector index, inverted file flat index, is the only type of neighbor partition index supported. It is a partition-based index which balances high search quality with reasonable speed.

In order for you to be able to use vector indexes, you do need to enable the vector pool area. And in order to do that, what you need to do is set the vector memory size parameter.

You can set it at the container database level. And the PDB inherits it from the CDB. Now bear in mind that the database does have to be balanced when you set the vector pool.

Other considerations, vector indexes are stored in this pool, and vector metadata is also stored here. You do need to restart the database. So large vector indexes do need lots of RAM, and RAM constrains the vector index size. You should use IVF indexes when there is not enough RAM. IVF index is used both the buffer cache as well as disk.

04:05

Lois: Now, memory is definitely a key consideration, right? Can you share more about the memory requirements and considerations for working with vectors?

Brent: So to remind you, a vector is a numerical representation of text, images, audio, or video that encodes the features or semantic meaning of the data, instead of the actual contents, such as the words or pixels of an image. So the vector is a list of numerical values known as dimensions with a specified format.

Now, Oracle does support the int8 format, the float32 format, and the float64 format. Depending on the format depends on the number of bytes. For instance, int8 is one byte, float32 is four bytes.

04:56

Nikita: And how do you calculate the size of a vector?

Brent: Now, that's going to depend upon the embedding model that you use to create those embeddings. Oracle AI Vector Search supports vectors with up to 65,535 dimensions. As a reminder, vectors are stored in tables and table data is stored on disk.

05:19

Nikita: Let's talk about working with vectors in tables. Can you walk us through how Oracle Database 23ai supports creating tables with vector columns?

Brent: Now, Oracle Database 23ai does have a new VECTOR data type. The new data type was created in order to support vector search.

The definition can include the number of dimensions and can include the format. Bear in mind that either one of those are optional when you define your column. The possible dimension formats are Int, float 32, and float 64. Float 32 and float 64 are IEEE standards, and Oracle Database will automatically cast the value if needed.

Let's take a look at some of the declaration examples. Now, if we just do a vector type, then the vectors can have any arbitrary number of dimensions and formats. If we describe the vector type as vector * , *, then that means that vectors can have an arbitrary number of dimensions and formats. Vector and vector * , * are equivalent. Vector with the number of dimensions specified, followed by a comma, and then an asterisk, is equivalent to vector number of dimensions.

Vectors must all have the specified number of dimensions, or an error will be thrown. Every vector will have its dimension stored without format modification. And if we do vector asterisk common dimension element format, what that means is that vectors can have an arbitrary number of dimensions, but their format will be up-converted or down-converted to the specified dimension element format, either INT8, float 32, or float 64.

07:25

Lois: Are there any operations or configurations that are prohibited with the VECTOR data type?

Brent: You cannot define vector columns in or as external tables, index-organized tables, neither as the primary key nor as non-key columns, in clusters or cluster tables, global temporary tables, subpartitioning key, primary key, foreign key, or unique constraint. Additionally, you cannot define vector columns in or as check constraints, default value, modify column, manually segment space manage tablespaces. Only the SYS user can create vectors as basic files in manually segment space manage tablespaces. For continuous query notification queries, or for non-vector indexes such as B-tree, bitmap, reverse key, text, or spatial indexes. Also, bear in mind that Oracle does not support distinct, count distinct, order by, group by, join condition, or comparison operators such as less than, greater than, or equal to with vector columns.

08:46

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09:12

Nikita: Welcome back!! Now, let's shift gears and discuss vector search itself. How does one create a vector "on the fly" for testing or learning purposes?

Brent: Now, the vector constructor is a function that allows us to create vectors without having to store those in a column in a table. These are useful for learning purposes. You use these usually with a smaller number of dimensions. Bear in mind that most embedding models can contain thousands of different dimensions. You get to specify the vector values, and they usually represent two-dimensional like xy coordinates. The dimensions are optional, and the format is optional as well.

10:01

Lois: Once we have vectors, how do we compare them or measure how "close" they are to each other?

Brent: Now vector distance uses the function VECTOR_DISTANCE as the main function. This allows you to calculate distances between two vectors and therefore takes two vectors as parameters. Optionally, you can specify a metric. If you do not specify a metric, then the default metric, COSINE, would be used.

You can optionally use other shorthand functions, too. These include L1 distance, L2 distance, cosine distance, and inner product. All of these functions also take two vectors as input and return the distance between them. Now the VECTOR_DISTANCE function can be used to perform a similarity search. And bear in mind these caveats. If a similarity search query does not specify a distance metric, then the default cosine metric will be used for both exact and approximate searches.

If a similarity search does specify a distance metric in the VECTOR_DISTANCE function, then an exact search with that distance metric is used if it conflicts with the distance metric specified in a vector index. If the two distance metrics are the same, then this will be used for both exact as well as approximate searches.

11:44

Nikita: Can you break down the distance metrics we use in Oracle AI Vector Search?

Brent: We have Euclidean and Euclidean squared distances. We have cosine similarity, dot product similarity, Manhattan distance, and Hamming similarity. Now let's take a closer look at the first of these metrics, Euclidean and Euclidean squared distances. This gives us the straight-line distance between two vectors. It does use the Pythagorean theorem. And notice that it is sensitive to both the vector size as well as the direction.

With Euclidean distances, comparing squared distances is equivalent to comparing distances. So when ordering is more important than the distance values themselves, the squared Euclidean distance is very useful as it is faster to calculate than the Euclidean distance, which avoids the square root calculation.

12:54

Lois: Cosine similarity is a term I hear often. How does it work exactly?

Brent: It is one of the most widely used similarity metrics, especially in natural language processing. The smaller the angle means they are more similar. While cosine distance measures how different two vectors are, cosine similarity measures how similar two vectors are.

13:20

Nikita: Dot product similarity comes up a lot, too. What's its role?

Brent: Dot product similarity allows us to multiply the size of each vector by the cosine of their angle. The corresponding geometrical interpretation of this definition is equivalent to multiplying the size of one of the vectors by the size of the projection of the second vector onto the first one or vice versa. Larger means that they are more similar. Smaller means that they are less similar.

13:58

Lois: How does Manhattan distance differ from other metrics, and when is it used?

Brent: This is useful for describing uniform grids. You can imagine yourself walking from point A to point B in a city such as Manhattan. Now, since there are buildings in the way, maybe we need to walk down one street and then turn and walk down the next street in order to get to our result. As you can imagine, this metric is most useful for vectors describing objects on a uniform grid such as city blocks, power grids, or perhaps a chessboard. Now these are faster than the Euclidean metric.

14:48

Nikita: And how is Hamming similarity different from the others?

Brent: This describes where vector dimensions differ. They are binary vectors, and it tells us the number of bits that require change to match. It compares the position of each bit in the sequence. Now, these are usually used in order to detect network errors.

15:17

Nikita: Now that we've covered the foundations, how do we actually search for the "closest" vectors in our data? What's an exact similarity search?

Brent: An exact similarity search allows you to calculate the query vector distance to all other vectors. This is also called a flat search or an exact search. This does give you the most accurate results. It gives you perfect search quality. However, you might have potentially long search times.

Now, this comparison is done using a particular distance metric. But what is important is the result set of your top closest vectors not the distance between them.

Let's take a look at one of the metrics. This one is Euclidean. The Euclidean similarity search retrieves the top k nearest vectors in your space relative to the Euclidean distance metric and a query vector.

Now let's take a look at Euclidean squared distance. In the case of Euclidean distances, comparing squared distances is equivalent to comparing distances. So when ordering is more important than the distance values themselves, the Euclidean squared distance is very useful, as it is faster to calculate than the Euclidean distance, avoiding the square-root calculation.

16:46

Lois: How does that compare to approximate searches, which are usually faster, using vector indices?

Brent: Approximate similarity search is a type of vector search that uses vector indexes. In order to use vector indexes, you have to ensure that you have enabled the vector pool in the SGA. For a vector search to be useful, it needs to be fast and accurate. These types of searches can be more efficient. However, the trade off is that they can be less accurate. Now, approximate searches use vector indexes, and there are many types of approximate searches that you can perform using vector indexes. Vector indexes can be less accurate, but they can consume less resources. Because 100% accuracy cannot be guaranteed by the heuristics, vector index searches use target accuracy.

Internally, the algorithms used for both the index creation and index search are doing their best to be as accurate as possible. You do have the option to influence those algorithms by specifying a target accuracy.

Let's take a look at vector indexes a little closer. We have two types of vector indexes. We have HNSW indexes, which stand for Hierarchical Navigable Small World index, and we have Inverted File Flat index, or IVF.

18:23

Nikita: And for more complex requirements, how does Oracle handle multi-vector similarity search?

Brent: Multi-vector similarity search is usually used for multi-document search. The documents would be split into chunks. The chunks would be embedded individually into vectors. It does use the concept of groupings called partitions. A multi-vector search consists of retrieving the top K vector matches, using the partitions based on the document's characteristics.

The ability to score documents based on the similarity of their chunks to a query vector being searched is facilitated in SQL using the partitioned row-limiting clause.

Now, the partition row-limiting clause extension is a generic extension of the SQL language. It does not have to apply to just vector searches. Multi-vector search with the partitioning row limit clause does not use vector indexes.

19:32

Lois: We covered quite a lot today! Thanks for that, Brent! If you want to learn more about the topics we discussed today, go to mylearn.oracle.com and search for the Oracle AI Vector Search Fundamentals course. Until next time, 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.