Episodes

  • Encore: Cloud Data Centers - Core Concepts Part 2
    May 5 2026
    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...
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    14 mins
  • Encore: Cloud Data Centers - Core Concepts Part 1
    Apr 28 2026
    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? ...
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    17 mins
  • Vector AI Supporting Features: What's New in Oracle Exadata and GoldenGate
    Apr 22 2026
    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 ...
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    14 mins
  • RAG with Oracle AI Vector Search and OCI Generative AI: Python and PL/SQL Approaches
    Apr 14 2026
    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 Here's some exciting news! Oracle University has training to help your teams unlock Redwood—the next-gen design system for Fusion ...
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    11 mins
  • Retrieval Augmented Generation (RAG)
    Apr 7 2026
    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 ...
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    13 mins
  • Inside Oracle AI Vector Search: Indexes, Metrics, and Best Practices
    Mar 31 2026
    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...
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    20 mins
  • Introduction to Oracle AI Vector Search
    Mar 24 2026
    Explore Oracle AI Vector Search and learn how to find data by meaning, not just keywords, using powerful vector embeddings within Oracle Database 23ai. In this episode, hosts Lois Houston and Nikita Abraham, along with Senior Principal APEX & Apps Dev Instructor Brent Dayley, break down how similarity search works, the new VECTOR data type, and practical steps for implementing secure, AI-powered search across both structured and unstructured data. 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. ---------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! Today, we're beginning a brand-new season, this time on Oracle AI Vector Search. Whether you're new to vector searches or you've already been experimenting with AI and data, this episode will help you understand why Oracle's approach is such a game-changer. Lois: To make sure we're all starting from the same place, here's a quick overview. Oracle AI Vector Search lets you go beyond traditional database searches. Not only can you find data based on specific attribute values or keywords, but you can also search by meaning, using the semantics of your data, which opens up a whole new world of possibilities. 01:20 Nikita: That's right, Lois. And guiding us through this episode is Senior Principal APEX & Apps Dev Instructor Brent Dayley. Hi Brent! What's unique about Oracle's approach to vector search? What are the big benefits? Brent: Now one of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data, all in one single system. This is very powerful, and also a lot more effective because you don't need to add a specialized vector database. And this eliminates the pain of data fragmentation between multiple systems. It also supports Retrieval Augmented Generation, also known as RAG. Now this is a breakthrough generative AI technique that combines large language models and private business data. And this allows you to deliver responses to natural language questions. RAG provides higher accuracy and avoids having to expose private data by including it in the large language model training data. 02:41 Lois: OK, and can you explain what the new VECTOR data type is? Brent: So, this data type was introduced in Oracle Database 23ai. And it allows you to store vector embeddings alongside other business data. Now, the vector data type allows a foundation to store vector embeddings. This allows you to store your business data in the database alongside your unstructured data, and allows you to use those in your queries. So it allows you to apply semantic queries on business data. 03:24 Lois: For many of our listeners, "vector embeddings" might be a new term. Can you explain what vector embeddings are? Brent: Vector embeddings are mathematical representations of data points. They assign mathematical representations based on meaning and context of your unstructured data. You have to generate vector embeddings from your unstructured data either outside or within the Oracle Database. In order to get vector embeddings, you can either use ONNX embedding machine learning models or access third-party REST APIs. Embeddings can be used to represent almost any type of data, including text, audio, or visual such as pictures. And they are used in proximity searches. 04:19 Nikita: Now, searching with these embeddings isn't about looking for exact matches like traditional search, right? This is more about meaning and similarity, even when the words or images differ? Brent, how does similarity search work in this context? Brent: So vector data is usually unevenly distributed and clustered. Vector data tends to be unevenly distributed and clustered into groups that are semantically related. Doing a similarity search based on a given query vector is equivalent to retrieving the k nearest vectors to your query vector in your vector space. What this means is that basically you need to find an ordered list of vectors by ranking them, where the first row is the closest or most similar vector to the query ...
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    15 mins
  • Exploring the Oracle Analytics AI Assistant
    Mar 17 2026
    Join hosts Lois Houston and Nikita Abraham for a special episode of the Oracle University Podcast as they explore the Oracle Analytics AI Assistant. In this episode, you'll discover how Oracle's AI-powered conversational tool empowers users of all backgrounds to interact with business data using simple, natural-language questions. Learn how the assistant interprets queries, surfaces visualizations, and delivers actionable insights in seconds, all within Oracle's secure analytics environment. The episode dives into best practices for data preparation, security and privacy safeguards, how to configure datasets for optimal AI performance, and tips for getting the most relevant results. You'll also hear how synonyms, column indexing, and user permissions make analytics more accessible and accurate. Visualize Data with the Oracle Analytics AI Assistant: https://mylearn.oracle.com/ou/article-course/visualize-data-with-the-oracle-analytics-ai-assistant/156941/ 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. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! Today's episode is on the Oracle Analytics AI Assistant, which is all about making business data accessible and useful, no matter your background. Whether you're a seasoned pro or just starting out with Oracle Analytics, you'll want to stick around for this episode because we're covering everything you need to know to unlock powerful, intuitive, and secure data insights. 01:06 Lois: That's right. And full disclosure before we start. We're trying something a little different for this episode. Instead of a live guest, our expert will be an AI-generated voice sharing insights drawn directly from Oracle's official course materials. Think of it as getting a taste of what our training courses are like, with a little help from AI. So, with that, let's kick things off by taking a closer look at what the Oracle Analytics AI Assistant really is. Expert: The Oracle Analytics AI Assistant is an AI-powered tool that provides a conversational interface for data analysis. With this tool, data exploration becomes more intuitive and efficient, helping you access fast, personalized insights. The AI Assistant makes use of Generative AI to process queries, analyze indexed datasets, and create or refine relevant visualizations. It is fully integrated into the Oracle Analytics platform, complementing existing analytic and visualization capabilities. 02:13 Nikita: So, put simply, users have the ability to interact with their data in plain English and receive immediate, visual answers. Expert: Exactly! You can ask natural language questions, such as, "What were my sales in the United States last Tuesday?" or "Show me monthly sales for this year," and the assistant interprets the question, queries the right data, and generates the best visualization. 02:39 Lois: Before we dive deeper, let's ground ourselves in some of the core concepts behind this technology. Here's an overview of the AI technologies powering the assistant. Expert: - Artificial Intelligence refers to systems or machines that perform tasks which typically require human intelligence, like reasoning, learning, perception, and language understanding. - Large Language Models or LLMs are AI programs trained on very large data sets. LLMs can generate human-like language and perform complex language tasks, such as writing emails or answering questions. - Generative AI is a branch of AI that can create new content, such as text, images, and audio. GenAI includes chatbots and virtual assistants capable of human-like conversations, answering questions, and creating content based on user prompts. - Natural Language Processing or NLP is a subfield of AI, targeting how computers understand and generate human language. 03:42 Lois: Now, let's look at what happens behind the scenes when someone interacts with the Oracle Analytics AI Assistant. Expert: Here is how the process works. You ask a question or make a request in natural language. Oracle Analytics Cloud identifies the most relevant dataset to answer that question, looking at metadata and attribute values. The platform prepares a prompt for the LLM that includes dataset metadata, column names, ...
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    18 mins