--- title: RheoData Blog | 23ai description: 23ai | RheoData Blog Posts --- [![RheoData - Logo - transparent-1](https://rheodata.com/hs-fs/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png?width=250&height=50&name=RheoData%20-%20Logo%20-%20transparent-1.png "RheoData - Logo - transparent-1")](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - 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Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - [Exadata & Oracle Database](https://rheodata.com/exadata-and-oracle-database-26ai) - Verticals - [Manufacturing](https://rheodata.com/manufacturing) - [Retail](https://rheodata.com/retail) - [State & Local](https://rheodata.com/sled) - [Customer Stories](https://rheodata.com/customer-stories) - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration) - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) Posts about # 23ai ## [Oracle GoldenGate 23ai – Permissions, what to know!](https://rheodata.com/en-us/blog/ogg-23ai-permissions) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:44 PM One of the biggest issues with Oracle GoldenGate over the years has been the database permissions... [CONTINUE READING](https://rheodata.com/en-us/blog/ogg-23ai-permissions) ## [Building a Data Governance Framework](https://rheodata.com/en-us/blog/build-data-governance-framework) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:06 PM # Building a Data Governance Framework Data governance is crucial for any organization looking to... [CONTINUE READING](https://rheodata.com/en-us/blog/build-data-governance-framework) ## [AI Gets a Real-Time Boost: GoldenGate Powers RAG with Fresh Vectors](https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:32 PM Artificial intelligence (AI) is transforming how businesses operate, and one of the most exciting... [CONTINUE READING](https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag) ## [The age of AI with Oracle GoldenGate23ai](https://rheodata.com/en-us/blog/ai-goldengate23ai) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:20 PM Artificial Intelligence (AI) is buzzing in today’s enterprise circles. Let’s briefly examine using... [CONTINUE READING](https://rheodata.com/en-us/blog/ai-goldengate23ai) ## [Data Governance Explained](https://rheodata.com/en-us/blog/data-governance-explained) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:18 PM # Data Governance Explained Data governance is the overall management of the availability, usability,... [CONTINUE READING](https://rheodata.com/en-us/blog/data-governance-explained) ## [The Importance of Fresh Data for LLMs](https://rheodata.com/en-us/blog/importance-of-fresh-data-llms) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:14 PM Large Language Models (LLMs) have revolutionized many aspects of technology and business. However,... [CONTINUE READING](https://rheodata.com/en-us/blog/importance-of-fresh-data-llms) ## [AI Benefits Using Oracle GoldenGate to Replicate Vectors](https://rheodata.com/en-us/blog/ai-benefits-using-ogg-vectors) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:10 PM ## Introduction [CONTINUE READING](https://rheodata.com/en-us/blog/ai-benefits-using-ogg-vectors) ## [AI Ethics in Today’s Marketplace](https://rheodata.com/en-us/blog/ai-ethics-today) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:28:55 PM As AI Experts,RheoData understands the increasing integration of Artificial Intelligence (AI) into... [CONTINUE READING](https://rheodata.com/en-us/blog/ai-ethics-today) ## [Data Governance with Oracle GoldenGate](https://rheodata.com/en-us/blog/data-governance-with-ogg) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:20:31 PM [CONTINUE READING](https://rheodata.com/en-us/blog/data-governance-with-ogg) ### Recent Posts #### [Coordinated Replicats: Faster, Lower-Risk GoldenGate Loads](https://rheodata.com/en-us/blog/coordinated-replicats-initial-load) Posted at Jun 26, 2026 11:08:13 AM ![Post Featured Image](https://rheodata.com/hubfs/Gemini_Generated_Image_509fjc509fjc509f.png) #### [Forward Deployed Engineering: The Operating Model the Agentic Era Demands](https://rheodata.com/en-us/blog/forward-deployed-engineering-agentic-era) Posted at May 30, 2026 11:46:34 AM ![Post Featured Image](https://rheodata.com/hubfs/Gemini_Generated_Image_r8vpntr8vpntr8vp.png) #### [The Bowl Is Broken: A CEO's Note on Mental Health Month and Tech Team Burnout](https://rheodata.com/en-us/blog/tech-team-burnout-mental-health-month-2026) Posted at May 12, 2026 7:23:33 AM ![Post Featured Image](https://rheodata.com/hubfs/IMG_2142.jpg) ### Posts by Tag - 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All Rights Reserved. ```json { "@context" : "https://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "US", "addressRegion" : "GA" }, "description" : "Elite specialist partner for Oracle, Snowflake, BigQuery, and AI-ready data platforms.", "email" : "hello@rheodata.com", "foundingDate" : "2020", "logo" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png", "name" : "RheoData", "sameAs" : [ "https://www.linkedin.com/company/rheodata" ], "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "WebSite", "name" : "RheoData", "potentialAction" : { "@type" : "SearchAction", "query-input" : "required name=search_term_string", "target" : "https://rheodata.com/search?q={search_term_string}" }, "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "", "url" : "" }, "dateModified" : "2026-04-17T17:31:04+0000", "datePublished" : "2025-10-30T17:41:53+0000", "description" : "23ai | RheoData Blog Posts", "headline" : "<span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text" >rheodata_blog listing page</span>", "image" : "", "mainEntityOfPage" : { "@id" : "https://rheodata.com/en-us/blog/tag/23ai", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "One of the biggest issues with Oracle GoldenGate over the years has been the database permissions needed for the GoldenGate Admin to use the product. Over the years, many users, admins, and consultants have tried to write scripts that would alleviate this concern. Ultimately, some manager or DBA would say “screw it” and grant the SYSDBA role to the GoldenGate user just to ensure that everything worked as expected. Outside of assigning SYSDBA to the GoldenGate admin, many others and I have scripts that look like this: For Oracle Database 12c through 21c (CDB/PDB configuration): --Run from the CDB layer — create user C##GGATE identified by *************** default tablespace USERS temporary tablespace TEMP quota unlimited on USERS account unlock; grant connect to c##ggate; grant dba to c##ggate; grant resource to c##ggate; grant alter any table to c##ggate; grant alter session to c##ggate; grant alter system to c##ggate; grant create any edition to c##ggate; grant create evaluation context to c##ggate; grant create job to c##ggate; grant create rule to c##ggate; grant create rule set to c##ggate; grant create session to c##ggate; grant dequeue any queue to c##ggate; grant drop any edition to c##ggate; grant execute any rule set to c##ggate; grant flashback any table to c##ggate; grant insert any table to c##ggate; grant logmining to c##ggate; grant select any dictionary to c##ggate; grant select any table to c##ggate; grant select any transaction to c##ggate; grant unlimited tablespace to c##ggate; --Modify this line for the correct PDB— begin SYS.DBMS_GOLDENGATE_AUTH.GRANT_ADMIN_PRIVILEGE('C##GGATE', container=>'ALL’); end; / As you can tell, although the roles of CONNECT, DBA, and RESOURCE were granted; there were still another 19 grants that needed to be provided to the GoldenGate user at the CDB level. At the PDB level, the following needs to be granted to the GoldenGate User: --Modify this line for the correct PDB— alter session set container = OGGTST; grant connect to c##ggate; grant dba to c##ggate; create user GGATE identified by *************** default tablespace USERS temporary tablespace TEMP quota unlimited on USERS account unlock; grant connect to ggate; grant dba to ggate; This meant that the Common GoldenGate (C##GGATE) user needed access to the underlying PDB plus a separate GoldenGate user (GGATE) was needed for the Replicat to apply to the PDB. Plus, the local GoldenGate user was granted the CONNECT and DBA roles. In both cases, granting the DBA role to the GoldenGate common user and local user was a potential security issue. Starting on Oracle GoldenGate 23ai, the product team has finally done something about this! I, for one, am happy that they made some changes to help mitigate this problem. Sadly, these changes only work with Oracle Database 23ai. Meaning that if you use Oracle GoldenGate 23ai against an earlier version of the Oracle Database (12c – 21c), you will still use a script similar to what I showed previously. Moving towards 23ai Starting in Oracle Database 23ai, the database now provides Oracle GoldenGate roles that you can grant to a GoldenGate user. Additionally, in 23ai you can now do per-PDB capture, which makes simplifies the configuration back to just a single GoldenGate user. The roles that Oracle is now providing for Oracle GoldenGate 23ai are as follows: OGG_CAPTURE – privileges necessary for using and managing Extract processes. OGG_APPLY – privileges necessary for using and managing Replicat processes. OGG_APPLY_PROCREP – privileges necessary to execute packages supported for procedural replication. OGG_CAPTURE For the GoldenGate user to capture from the PDB, the following example shows how to configure the user: GRANT CONNECT TO GGATE; GRANT RESOURCE TO GGATE; GRANT OGG_CAPTURE TO GGATE; If the GoldenGate user is configured against a CDB, then an ALTER USER command must be executed as well: ALTER USER C##GGATE SET CONTAINER_DATA=ALL CONTAINER=CURRENT; Note: I tried this, and it works only from the AdminClient. The HTML5 pages will not work for setting this permission. OGG_APPLY For the GoldenGate user to apply transactions to the database, the following example shows how to configure the user: GRANT CONNECT TO GGATE; GRANT RESOURCE TO GGATE; GRANT OGG_APPLY TO GGATE; GRANT SELECT, INSERT, UPDATE, DELETE ON {schema}.{table} to GGATE; … … GRANT SELECT, INSERT, UPDATE, DELETE ON {schema}.{table} to GGATE; If the GoldenGate user needs to do DDL operations, then the appropriate permissions must be set as well. Here is an example: GRANT CREATE TABLE, ALTER TABLE, DROP TABLE to GGATE; OGG_APPLY_PROCREP The last permission that can be assigned is related to a feature that came out in Oracle GoldenGate 12.3.0.1, called Procedural Replication. Procedural Replication is cool and may be another blog post later. In the meantime, this role should be assigned in conjunction with OGG_APPLY if there is a need for the user that runs the Replicat to execute procedures on the target platform. An example of granting this role is as follows: GRANT CONNECT, RESOURCE TO GGATE; GRANT OGG_APPLY, OGG_APPLY_PROCREP TO GGATE; Summary With Oracle GoldenGate 23ai there are a few good things that come with the release. One of which is the database roles that can be assigned to make the configuration simpler, although it is only in Oracle Database 23ai. With the attempt to make it simpler, I feel that Oracle made it both complex and simpler. What I mean is that assigning the OGG_APPLY role only gets you so far. If you do not assign the DML permissions on a per table basis, you will run into “ORA-41900: missing privilege”. Possible Workaround With the new way of assigning permissions with Oracle GoldenGate 23ai, there may be a workaround to solving the DML permissions problem. By lumping all the required permission into a separate role, you may be able to simplify the assignment process. Just my opinion and haven’t tried it yet.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Oracle GoldenGate 23ai – Permissions, what to know!", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/ogg23ai-permissions-1-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ogg-23ai-permissions", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Building a Data Governance Framework Data governance is crucial for any organization looking to leverage its data effectively. It provides a structured approach to managing, using, and protecting data assets. Let’s break down how to build a data governance framework in a simple and understandable way. What is Data Governance? Data governance is the overall management of the availability, usability, integrity, and security of data in an enterprise. It involves establishing policies, procedures, and standards to ensure data is consistent, reliable, and trustworthy. Key Components of a Data Governance Framework Here are the essential components you need to focus on when building your framework: 1. Define Goals and Objectives First, determine what you want to achieve with data governance. What are the business drivers? Common goals include: Improving data quality Ensuring regulatory compliance Enhancing decision-making Increasing data security 2. Identify Roles and Responsibilities Clearly define roles and responsibilities for data governance. Key roles include: Data Owner: Responsible for data definition, quality, and usage. Data Steward: Implements data policies and procedures. Data Custodian: Manages the technical aspects of data storage and access. Data Governance Council: Oversees the data governance program. 3. Establish Data Policies and Standards Create policies and standards that govern how data is managed and used. These should cover: Data quality rules Data security and privacy Data access and usage Data retention and disposal 4. Implement Data Quality Management Data quality is essential. Implement processes to: Identify and correct data errors Monitor data quality metrics Establish data validation rules 5. Develop a Data Dictionary and Metadata Management Create a data dictionary to document data elements, definitions, and relationships. This helps ensure everyone is on the same page. 6. Establish Communication and Training Ensure everyone in the organization understands the data governance framework. Provide training and regular communication to promote awareness and adherence. 7. Monitor and Measure Success Continuously monitor the effectiveness of your data governance framework. Track key metrics and make adjustments as needed. Steps to Implement Your Framework Here’s a simple step-by-step approach: Assessment: Evaluate your current data management practices. Planning: Define goals, roles, policies, and standards. Implementation: Roll out the framework and provide training. Monitoring: Track metrics and make improvements. Continuous Improvement: Regularly review and update the framework. Why Data Governance Matters Effective data governance leads to better decision-making, improved data quality, and reduced risk. It empowers organizations to use data as a strategic asset. By following these steps, you can build a robust and effective data governance framework that supports your organization’s goals.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Building a Data Governance Framework", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1040748327-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/build-data-governance-framework", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Artificial intelligence (AI) is transforming how businesses operate, and one of the most exciting areas is Retrieval Augmented Generation (RAG). RAG allows AI models to answer questions and generate content by accessing and referencing external data, making them far more accurate and relevant. But for RAG to truly shine, that external data needs to be up-to-date. That’s where Oracle GoldenGate comes in, especially when dealing with vector databases. What’s the Connection? Vectors and RAG Vectors: AI models often represent data as “vectors” — numerical representations of information. These vectors allow AI to understand the relationships and similarities between different pieces of data. Vector Databases: These specialized databases store and manage vectors, enabling fast and efficient retrieval of relevant information for AI tasks. RAG (Retrieval Augmented Generation): RAG systems use vector databases to find relevant context before generating a response. This allows AI to provide more accurate and contextually rich answers. The Challenge: Keeping Vectors Fresh The problem arises when the underlying data changes. If your vectors are based on outdated information, your AI’s responses will be inaccurate. This is where real-time replication is crucial. How Oracle GoldenGate Solves the Problem Oracle GoldenGate shines by providing real-time data replication, ensuring your vector databases are always synchronized with the source data. Here’s how it benefits AI applications: Real-Time Vector Updates: GoldenGate captures changes in the source data and instantly replicates those changes to your vector database. This ensures that your AI models are working with the most current information. Improved RAG Accuracy: By using fresh vectors, RAG systems can retrieve the most relevant context, leading to more accurate and reliable AI responses. Enhanced AI Performance: Real-time data replication minimizes latency and ensures that AI models have access to the information they need, when they need it. Automation: GoldenGate automates the replication process, reducing the need for manual data updates and minimizing the risk of errors. Diverse Data Support: GoldenGate can replicate data from a wide range of sources, including traditional databases and cloud-based systems, enabling you to integrate data from diverse sources into your vector databases Benefits in Action: Customer Service: Imagine a chatbot that uses RAG to answer customer questions. With GoldenGate, the chatbot can access real-time product information, ensuring accurate and up-to-date responses. Financial Analysis: AI models can use RAG to analyze financial data and identify trends. GoldenGate ensures that the models are working with the latest market data, enabling more accurate predictions. Content Creation: AI can create content based on current events. GoldenGate makes sure the AI has access to the most recent news. In Simple Terms: Oracle GoldenGate acts like a live wire, instantly transferring data changes to your AI’s memory (vector database). This means your AI always has the latest information, resulting in smarter, more accurate, and more helpful responses. By leveraging Oracle GoldenGate, organizations can unlock the full potential of RAG and build AI applications that are truly intelligent and responsive.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "AI Gets a Real-Time Boost: GoldenGate Powers RAG with Fresh Vectors", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1124857300-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Artificial Intelligence (AI) is buzzing in today’s enterprise circles. Let’s briefly examine using AI in data integration, specifically with Oracle GoldenGate. First, what are the benefits of AI in data integration? Using AI in data integration offers several significant benefits, enhancing data management processes’ efficiency, accuracy, and overall capabilities. Here are some of the key advantages: Automation of Repetitive Tasks: AI can automate routine data integration tasks, reducing the need for manual intervention. This includes data extraction, transformation, and loading (ETL) processes, which can be labor-intensive and error-prone when done manually. Improved Data Quality: AI algorithms can detect and correct errors, inconsistencies, and duplicates in data. Machine learning models can learn from historical data to identify patterns and anomalies, ensuring higher data quality. Enhanced Data Matching and Merging: AI can improve the accuracy of data matching and merging from different sources by using advanced algorithms to reconcile discrepancies and integrate data seamlessly. This is particularly useful when data comes from various formats and systems. Real-time Data Integration: AI can facilitate real-time data integration by continuously monitoring and updating data streams. This ensures that integrated data is always up to date, supporting real-time analytics and decision-making. Scalability: AI-driven data integration solutions can scale efficiently with growing data volumes and complexity. They can handle large datasets and complex data structures more effectively than traditional methods. Intelligent Data Mapping: AI can automate the process of data mapping, where data fields from different sources are aligned with each other. Machine learning models can infer and adapt to changes in data schema, reducing the need for manual mapping. Cost Efficiency: AI can significantly reduce operational costs by automating many aspects of data integration. It minimizes the need for extensive human resources and reduces errors that might lead to costly rectifications. Enhanced Data Governance and Compliance: AI can help maintain compliance with data governance standards and regulations by automatically enforcing data quality rules, auditing data usage, and ensuring proper data lineage tracking. Better Insights and Analytics: Integrated data enriched by AI capabilities allows for deeper insights and more sophisticated analytics. AI can help uncover hidden patterns, trends, and correlations within integrated datasets, leading to more informed business decisions. Handling Unstructured Data: AI is particularly adept at handling unstructured data (e.g., text, images, videos) and integrating it with structured data, providing a more comprehensive view of the data landscape. Personalization and Customization: AI can tailor data integration processes to specific business needs, learning and adapting to unique requirements and preferences, leading to more effective and relevant data integration solutions. By leveraging AI in data integration, organizations can achieve more reliable, efficient, and scalable data management, ultimately driving better business outcomes and gaining a competitive edge. These eleven benefits are all great in concept, but how do you put these into practice? Oracle GoldenGate has always been the interface for successful data integration for many organizations. With the recent rise in AI, many organizations seek solutions to build robust AI infrastructure with minimal downtime and maximum value. In reviewing all the data integration platforms out there – Oracle GoldenGate, FiveTran, Qlik, Airbyte, Striim, and many others – Oracle GoldenGate has the most robust and stable approach to tackling the new age of AI, although it is geared mainly towards Oracle and PostgreSQL workloads currently. With the release of Oracle GoldenGate 23ai, many are excited about getting to see AI at work; however, the 23ai in Oracle GoldenGate 23ai only means that Oracle GoldenGate can replicate the new datatype within Oracle Database 23ai – Vector datatype. The core replication concepts that Oracle GoldenGate follows are still the bedrock of replication. Since we touched on the vector datatype, let’s look at what vectors are and their benefits. Vectors Vectors are nothing new within the IT industry; after all, search engines have used different vectors for decades. The vectors being introduced now are high-dimensional numerical representations of data items. These vectors capture the semantic meaning of the data, enabling machines to understand and process the information effectively. The primary purpose of vectors is to use them within Retrieval Augmentation Generation (RAG) to facilitate efficient and accurate retrieval of relevant information from a large corpus of structured and unstructured data. How Vectors are used There are four steps to using a vector with Retrieval Augmentation Generation (RAG). The basic concepts are: Query Encoding: When a user inputs a query, it is transformed into a vector. This process is known as query encoding. Document Encoding: Similarly, all documents in the corpus are pre-encoded into vectors. Similarity Search: The vector of the query is compared against the vectors of the documents using similarity measures (e.g., cosine similarity). The documents with vectors most like the query vector are retrieved as relevant results. Generation: After the similarity search retrieves the relevant documents, these are used to augment the generative model. The generative model uses the information from the retrieved documents to generate a coherent and contextually appropriate response. Benefits of using Vectors with RAG systems Efficiency: Vectors allow for fast and efficient retrieval of information from large datasets. Accuracy: By capturing semantic meanings, vectors improve the accuracy of retrieval and the relevance of generated responses. Scalability: Vector-based retrieval scales well with increasing data sizes, maintaining performance even with large corpora. Contextual Understanding: Vectors enable the system to understand and leverage the context of queries and documents, enhancing the overall quality of generated output Now, with a brief understanding of what vectors are, how they are used, and their benefits, how does this apply to Oracle GoldenGate 23ai? Reviewing a simple uni-directional use case, like populating a data warehouse running Oracle Database 23ai (cloud or on-premises) in the manufacturing vertical. Basic replication steps Data is captured from data marts located at remote sites and applied to the data warehouse at a central site. The steps would look like: Source: Oracle GoldenGate (Extract) captures changed data from data marts. Source: Captured data is placed in trail files Source: Trail files are shipped across the network (if needed) Remote: Trail files are staged Remote: Trail files read by apply process Remote: Oracle GoldenGate (Replicat) applies changed data to the data warehouse Where does AI come in The six steps in the previous section are based on general replication principles defined in the CAP theorem. Under CAP, a data replication system ensures consistency (among replicated copies), availability (of the system for read/write operations), and partition tolerance (in the face of the nodes in the system being partitioned by a network fault). When overlaying AI onto these replication steps, it is nothing more than ensuring that vectors, the data that drive an RAG system, are available where needed for usage within an RAG system. In Oracle GoldenGate 23ai, Oracle has ensured that vectors can be captured, transferred, and applied to data platforms that support the vector datatype—enabling organizations to bring their real-time unstructured data to their AI applications and delivering the next-generation user experiences. Data Platforms that support Vectors Oracle GoldenGate 23ai has added to its extensive heterogeneous platforms by including popular vector databases in its growing portfolio. The new additions to the portfolio are: Oracle Database 23ai (OCI & On-Premises) MySQL Heatwave Postgres + pgVector (OCI) Postgres + pgVector EnterpriseDB + pgVector AlloyDB AmazonRDS AmazonAura Azure Postgres Elasticsearch OpenSearch Now that Oracle GoldenGate 23ai supports so many different Oracle and non-Oracle vector platforms, getting your mission-critical unstructured data to where it is needed is possible. The data integration patterns below can be used immediately after upgrading to Oracle GoldenGate 23ai. Migration of vectors to Oracle Database 23ai vector database Multi-Master/Multi-Cloud/Active-Active database replication Consolidation of vector changes With the ability to now take an organization’s real-time unstructured data, represented as vectors, an organization can move structured and unstructured data across the enterprise. As Artificial Intelligence (AI) starts to take hold in organizations, the typical use case that will leverage Oracle GoldenGate 23ai will be private Retrieval Augmented Generation (RAG) systems. These private RAG systems will leverage Oracle GoldenGate 23ai by moving structured and unstructured data to a central data hub where vectors will be leveraged against private large language models. An illustration of the architecture this would follow is: In this illustration, you can see that Oracle GoldenGate 23ai is moving data from various database sources to the data warehouse in real-time. Don’t let the illustration fool you; the sources that Oracle GoldenGate 23ai can capture include more than databases now. New capture types that can be supported include ERP & SaaS applications, Vector stores, Event Messaging, and NoSQL. Pitfalls Although Oracle GoldenGate 23ai makes it easy to move structured and unstructured data in real-time, including vectors, there is an inherent problem here. This problem is common with all vector databases – what embedding model is used? As AI begins to grow and be used more in enterprises, ensuring that the correct embedding model to embed data is used will be critical. Because we can now replicate vectors and enable Retrieval Augmented Generation (RAG) platforms with fresh and relevant data, it doesn’t mean that data sources can have different embeddings. What needs to be understood and combed are: What embedding model is being used at each site? How does an organization standardize an embedding model? How do you troubleshoot embedding models? Who is curating the embedding models and ensuring validity? These are only some of the questions that should be asked or evaluated before building a Retrieval Augmented Generation (RAG) system or implementing real-time replication of vectors with Oracle GoldenGate 23ai. In closing, Oracle GoldenGate 23ai is a leap forward in ensuring that enterprises can quickly and dynamically build Retrieval Augmented Generation (RAG) systems for their private and secure use cases.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "The age of AI with Oracle GoldenGate23ai", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/age-ai-gg23ai-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ai-goldengate23ai", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Data Governance Explained Data governance is the overall management of the availability, usability, integrity, and security of data in an organization. In simpler terms, it’s about making sure the right people have the right data at the right time, and that the data is accurate and protected. Why is Data Governance Important? Data is a critical asset for any organization. Effective data governance helps us: Make better decisions: Accurate and reliable data allows for informed strategic and operational decisions. Improve efficiency: Streamlined data processes reduce redundancy and waste. Ensure compliance: Adhering to regulations and standards protects the organization from legal risks. Enhance security: Protecting data from unauthorized access and breaches maintains trust and integrity. Key Components of Data Governance Here are the core elements that make up a strong data governance framework: Data Quality: Ensuring data is accurate, complete, and consistent. Data Security: Protecting data from unauthorized access, use, or disclosure. Data Stewardship: Assigning roles and responsibilities for data management. Data Policies: Establishing rules and guidelines for data use and handling. Data Standards: Defining common formats and definitions for data elements. How Data Governance Works Data governance involves a series of processes and activities, including: Defining data roles and responsibilities: Identifying who is accountable for different aspects of data. Establishing data policies and procedures: Creating guidelines for data management and use. Implementing data quality checks: Ensuring data accuracy and consistency. Monitoring data usage and compliance: Tracking how data is being used and ensuring adherence to policies. Benefits of Effective Data Governance Implementing a strong data governance program can lead to several significant benefits: Increased data trust: Confidence in the accuracy and reliability of data. Reduced operational costs: Streamlined data processes and reduced errors. Improved business agility: Faster access to reliable data for decision-making. Enhanced customer satisfaction: Better data leads to better services and experiences. Conclusion Data governance is not just an IT issue; it’s a business imperative. By establishing clear policies, roles, and processes, we can ensure that our organization’s data is a valuable asset that drives success. Investing in data governance is an investment in the future of our organization.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Data Governance Explained", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_973555365-scaled-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/data-governance-explained", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Large Language Models (LLMs) have revolutionized many aspects of technology and business. However, the effectiveness and accuracy of these models are heavily reliant on the quality and currency of the data they are trained on. Feeding LLMs with fresh data is not just a best practice, it is a critical requirement for ensuring their continued relevance and utility. Why Fresh Data Matters Accuracy and Relevance: LLMs trained on outdated data will produce outputs that reflect past information, not current realities. This can lead to inaccurate responses, irrelevant insights, and potentially misleading information. Fresh data ensures that the model’s knowledge base remains up-to-date, leading to more accurate and relevant outputs. Adaptation to Change: The world is constantly changing. New information emerges, trends shift, and language evolves. LLMs need to adapt to these changes to remain effective. By continuously feeding them with fresh data, we enable them to learn new patterns, understand emerging topics, and adjust to evolving language usage. Avoiding Bias and Stagnation: Data can become stale and biased over time. Using outdated data can perpetuate existing biases and prevent the model from learning about new perspectives or developments. Fresh data helps to mitigate these issues, ensuring that the LLM’s knowledge base is diverse and representative of the current state of the world. Improved Performance: LLMs trained on fresh data tend to perform better. They can provide more insightful analysis, generate more creative content, and offer more effective solutions to problems. This is because they are working with the most current information available, allowing them to make more informed decisions and predictions. Replication Tools for Fresh Data To keep LLMs up-to-date, it’s essential to have robust data pipelines that can continuously feed them with fresh information. Replication tools play a crucial role in this process. Here are two examples: Oracle GoldenGate Oracle GoldenGate is a comprehensive software solution for real-time data integration and replication. It enables the capture, transformation, and delivery of data between various databases and systems. Real-time Data Capture: GoldenGate captures changes to data as they occur, ensuring that the LLM always has access to the latest information. Heterogeneous Data Integration: It can replicate data across different database platforms, making it suitable for diverse data environments. Low Latency: GoldenGate provides near real-time data delivery, minimizing the time lag between data changes and their availability to the LLM. FiveTran FiveTran is a fully managed data pipeline platform that automates the extraction, loading, and transformation of data from various sources into a data warehouse. Automated Data Pipelines: FiveTran automates the entire data integration process, reducing manual effort and ensuring consistent data delivery. Wide Range of Connectors: It supports a vast array of data sources, including databases, SaaS applications, and APIs. Scalability and Reliability: FiveTran is designed to handle large volumes of data and ensures reliable data delivery, even in demanding environments. Both Oracle GoldenGate and FiveTran provide powerful capabilities for ensuring that LLMs are continuously fed with fresh data. By leveraging these tools, organizations can keep their LLMs up-to-date, accurate, and relevant, ultimately maximizing their value and impact.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "The Importance of Fresh Data for LLMs", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1175155504-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/importance-of-fresh-data-llms", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Introduction This document outlines the benefits of leveraging Oracle GoldenGate for replicating vectors to enhance AI applications. Oracle GoldenGate provides real-time data replication, enabling AI models to access the most current and relevant information. This capability is crucial for applications requiring up-to-date insights, such as fraud detection, personalized recommendations, and dynamic pricing. Benefits of Using Oracle GoldenGate for Vector Replication Real-Time Data Access: Oracle GoldenGate ensures that AI models receive real-time vector updates, allowing for timely and accurate decision-making. Scalability and Performance: GoldenGate can handle large volumes of data and high transaction rates, making it suitable for enterprise-level AI deployments. Data Consistency: GoldenGate maintains data consistency across different systems, ensuring that AI models are trained and operate on reliable data. Flexibility and Integration: GoldenGate supports various database platforms and cloud environments, providing flexibility in deploying AI solutions. Reduced Latency: By minimizing data transfer latency, GoldenGate enables AI applications to respond quickly to changes in the data. Guidelines for Improvement To optimize the use of Oracle GoldenGate for vector replication and enhance AI benefits, consider the following guidelines: Optimize Data Capture: Configure GoldenGate to capture only the relevant vector data, reducing the amount of data transferred and processed. Implement Data Filtering and Transformation: Use GoldenGate’s built-in capabilities to filter and transform data before replication, ensuring that AI models receive data in the required format. Monitor Replication Performance: Regularly monitor GoldenGate’s performance to identify and resolve any bottlenecks or issues that may affect data delivery to AI applications. Ensure Security: Implement robust security measures to protect sensitive vector data during replication. Automate Deployment and Management: Utilize automation tools to streamline the deployment and management of GoldenGate configurations for vector replication. Best Practices Here are some best practices for leveraging Oracle GoldenGate for vector replication: Define Clear Data Requirements: Clearly define the vector data required by AI models and configure GoldenGate accordingly. Test and Validate Replication: Thoroughly test and validate the replication process to ensure data accuracy and consistency. Document Configurations: Maintain comprehensive documentation of GoldenGate configurations for easy troubleshooting and maintenance. Stay Updated: Keep GoldenGate software and related components updated to benefit from the latest features and security patches. Conclusion Using Oracle GoldenGate for replicating vectors offers significant benefits for AI applications, including real-time data access, scalability, and data consistency. By following the outlined guidelines and best practices, organizations can optimize their AI deployments and achieve better outcomes.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "AI Benefits Using Oracle GoldenGate to Replicate Vectors", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1115479307-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ai-benefits-using-ogg-vectors", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "As AI Experts,RheoData understands the increasing integration of Artificial Intelligence (AI) into various aspects of business operations. With this integration comes the critical need to address AI ethics, particularly how AI should be used without bias to accomplish tasks set out by management. This post delves into the intricacies of AI ethics in the current marketplace. The Importance of Ethical AI AI has the potential to revolutionize industries, automate tasks, and provide valuable insights. However, if not implemented ethically, it can perpetuate biases, lead to unfair outcomes, and damage an organization’s reputation. Ethical AI ensures that AI systems are fair, transparent, and accountable. Identifying and Mitigating Bias in AI Bias in AI can arise from several sources, including biased training data, flawed algorithms, and human prejudices. To mitigate bias, organizations should: Diversify Training Data: Ensure that training data represents a wide range of demographics and perspectives. Regularly Audit AI Systems: Conduct regular audits to identify and rectify biases in AI algorithms. Implement Fairness Metrics: Use fairness metrics to assess the impact of AI decisions on different groups. AI and Management Tasks When using AI to accomplish tasks set out by management, it’s crucial to ensure that AI is aligned with ethical principles. Here are some guidelines: Define Clear Objectives: Clearly define the objectives of AI systems and ensure they align with ethical standards. Transparency: AI systems should be transparent, allowing stakeholders to understand how decisions are made. Accountability: Establish clear lines of accountability for AI decisions. Human Oversight: Implement human oversight to ensure AI systems are functioning as intended and not perpetuating biases. Best Practices for Ethical AI Implementation To ensure ethical AI implementation in the marketplace, consider these best practices: Develop an AI Ethics Framework: Create a comprehensive AI ethics framework that outlines principles and guidelines for AI development and deployment. Provide Training: Offer training to employees on AI ethics and bias mitigation. Engage Stakeholders: Involve stakeholders, including employees, customers, and the community, in discussions about AI ethics. Here is a table illustrating the key elements of ethical AI in the marketplace: By adhering to ethical principles and best practices, organizations can leverage the power of AI while ensuring fairness, transparency, and accountability.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "AI Ethics in Today’s Marketplace", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1167521146-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ai-ethics-today", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Introduction Data governance is crucial for maintaining data quality, security, and compliance. Oracle GoldenGate, a comprehensive data integration and replication software, plays a significant role in enforcing and supporting various data governance policies. How Oracle GoldenGate Supports Data Governance Oracle GoldenGate provides several features that aid in data governance: Data Replication and Distribution: Ensures data consistency across multiple systems, which is vital for maintaining a single source of truth. Data Transformation: Allows for data cleansing and standardization during replication, improving data quality and adherence to standards. Data Auditing and Tracking: Captures changes to data, providing an audit trail for compliance and accountability. Real-Time Data Delivery: Enables timely access to accurate data, supporting informed decision-making. Security Features: Includes encryption and secure data transfer, protecting sensitive information during replication. Key Features and Their Governance Implications Benefits of Using Oracle GoldenGate for Data Governance Improved Data Quality: Data transformation capabilities ensure consistent and accurate data across systems. Enhanced Data Security: Encryption and secure transfer protocols protect sensitive data. Regulatory Compliance: Audit trails and data tracking support compliance with various regulations. Efficient Data Management: Real-time replication and distribution streamline data management processes. Increased Data Accessibility: Timely data delivery enables better decision-making. Conclusion Oracle GoldenGate is a powerful tool that significantly supports data governance efforts. Its features for data replication, transformation, auditing, and security contribute to maintaining high data quality, ensuring compliance, and enabling efficient data management. By leveraging Oracle GoldenGate, organizations can establish a robust data governance framework that promotes trust and reliability in their data.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Data Governance with Oracle GoldenGate", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_335703289-scaled-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/data-governance-with-ogg", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ```