--- title: RheoData Blog | GoldenGate (6) description: GoldenGate | RheoData Blog Posts (6) --- [![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 # GoldenGate (6) ## [Patch Oracle GoldenGate Microservices using RESTful APIs](https://rheodata.com/en-us/blog/patch-oracle-goldengate-microservices-using-restful-apis) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:21 PM In 2017, Oracle introduced the world to Oracle GoldenGate Microservices through the release of... [CONTINUE READING](https://rheodata.com/en-us/blog/patch-oracle-goldengate-microservices-using-restful-apis) ## [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) ## [GoldenGate Initial Load Using Datapump Import Performance Improvement for IOT with 1.9B rows](https://rheodata.com/en-us/blog/goldengate-initial-load-using-datapump-import-performance-improvement-for-iot-with-1-9b-rows) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:16 PM Datapump is used for Instantiating Oracle GoldenGate with an Initial Load. [CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-initial-load-using-datapump-import-performance-improvement-for-iot-with-1-9b-rows) ## [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) ## [When Your Oracle LOBs Won’t Play Nice with Snowflake: A Real-World Solution](https://rheodata.com/en-us/blog/oracle-lob-replication-snowflake-goldengate-solution) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:13 PM This week, I found myself deep in a familiar challenge – helping a client navigate the complexities... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-lob-replication-snowflake-goldengate-solution) ## [Oracle to Snowflake: Your Complete Guide to Real-Time Data Integration with Oracle GoldenGate](https://rheodata.com/en-us/blog/oracle-to-snowflake-exec-complete-guide) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:12 PM “Our Oracle databases are drowning in decades of business data, and our analysts are spending more... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-to-snowflake-exec-complete-guide) ## [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) ## [Silent install for Oracle GoldenGate 21c (Big Data – Microservices edition) – Step 1 of 2](https://rheodata.com/en-us/blog/silent-install-for-oracle-goldengate-21c-big-data-microservices-edition-step-1-of-2) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:09 PM Been awhile since I did a post on installing Oracle GoldenGate. The last post I did was back in... [CONTINUE READING](https://rheodata.com/en-us/blog/silent-install-for-oracle-goldengate-21c-big-data-microservices-edition-step-1-of-2) ## [GoldenGate Service Monitor 0.1.0](https://rheodata.com/en-us/blog/goldengate-service-monitor-0-1-0) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:07 PM With the introduction of Oracle GoldenGate (Microservices), Oracle opened a lot of doors for people... [CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-service-monitor-0-1-0) ### 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 - [Cloud (88)](https://rheodata.com/en-us/blog/tag/cloud) - [GoldenGate (73)](https://rheodata.com/en-us/blog/tag/goldengate) - 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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" : "GoldenGate | RheoData Blog Posts (6)", "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/goldengate/page/6", "@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", "headline": "Patch Oracle GoldenGate Microservices using RESTful APIs", "mainEntityOfPage": "https://rheodata.com/en-us/blog/patch-oracle-goldengate-microservices-using-restful-apis", "datePublished": "10/11/2025", "dateModified": "10/11/2025", "articleBody": "In 2017, Oracle introduced the world to Oracle GoldenGate Microservices through the release of Oracle GoldenGate 12c (12.3.0.0.1). Upon the initial release, Oracle promoted the benefit of using the Microservices Architecture for new platforms and the ease of upgrading and patching. This document will show how an organization can start on one version of Oracle GoldenGate, install another version and migrate to that version with ease. Platform Oracle GoldenGate (Microservices) is now available on all the platforms Oracle supports for replication. This means organizations should have already installed Oracle GoldenGate (Microservices) and have environments replicating data. If so, then there will be times that these environments need to be upgraded or patched. To make upgrading or patching easier, Oracle has implemented an out-of-place upgrade or patching approach for Oracle GoldenGate. With any Oracle GoldenGate (Microservices) environment, there are two home directories that an administrator needs to be concerned with. These homes are: $OGG_HOME -> Oracle GoldenGate Home $DEPLOYMENT_HOME -> Oracle GoldenGate Deployment Home These homes are essential for Oracle GoldenGate (Microservices) to run. However, when it comes to upgrading or patching, only the Oracle GoldenGate Home ($OGG_HOME) is required. When you review Oracle GoldenGate Home, organizations will notice that the directory structure mimics a Linux/Unix filesystem-based similar to the Oracle Database. This approach makes it easier for Oracle GoldenGate to merge into the patching processes that are already industry standards. At the same time, by only needing the Oracle GoldenGate Home for patching, there are no interruptions to existing Oracle GoldenGate processing. This approach lets you install a second Oracle GoldenGate Home, the patch that home, then migrates processes over to the new home. You are facilitating a near-zero downtime patching approach. Patching Organizations need to patch their Oracle GoldenGate Homes for patching to be effective. To do this without taking an outage within the replication environment, installing a second Oracle GoldenGate Home is key. Second Home To install a second Oracle GoldenGate Home, organizations need to install the Oracle GoldenGate (Microservices) binaries in a new directory structure (outside of their existing Oracle GoldenGate Home). This can be done by doing the following: Download the required binaries Install binaries into the new Oracle GoldenGate Home After downloading the required binaries, these binaries can be installed using the same processes that an organization has established. For this example use case, installing the binaries is done by a silent install process. See below: Update the Oracle GoldenGate Core response file (oggcore.rsp) Install the new Oracle GoldenGate Home$ cd /tmp/ogg21cma/ggs*/Disk1 $ ./runInstaller -silent -ignoreSysPrereqs -ignorePrereq -showProgress -waitForCompletion -responseFile /tmp/oggcore1.rsp Once the install is done, there are now two Oracle GoldenGate (Microservices) homes within the environment. PatchSet With both Oracle GoldenGate Homes in place, the next step is to patch the Second Home with the patches required. The following steps are used to install the Oracle GoldenGate patch to the Second Home: Copy the patch set over to the host where Oracle GoldenGate (Microservices) runs. Use any tool that will facilitate moving the zip file. Unzip the patch in the temp directory $ unzip -q ./ p33846655_215000_Linux-x86-64.zip -d . Apply the patch $ export ORACLE_HOME=$ORACLE_HOME $ cd /tmp/33846655 $ $ORACLE_HOME/OPatch/opatch apply Validate that the patch has been applied$ $ORACLE_HOME/OPatch/opatch lsinventory After validating that the patch has been installed, the next thing is to migrate the ServiceManager and associated deployments to the new Oracle GoldenGate (Microservices) Home. List Deployments With the new Oracle GoldenGate (Microservices) Home patched, each corresponding deployment home must be migrated to the latest Oracle GoldenGate (Microservices) Home. This can be done in two different approaches – GUI and RESTful API. The most straightforward approach to performing the migration is using RESTful APIs. Before migrating any deployments between Oracle GoldenGate (Microservices) Homes, it is good to see what deployments exist on the server. This can be done using the following CURL command: curl –location –request GET ‘HTTP://:/services/v2/installation/deployments’ \ –header ‘Authorization: Basic b2dnYWRtaW46V0VsY29tZTEyMzQ1IyM=’ The resulting output would be a list of deployments that the ServiceManager is responsible for: { “$schema”: “api:standardResponse”, “links”: [ { “rel”: “canonical”, “href”: “http://:/services/v2/installation/deployments”, “mediaType”: “application/json” }, { “rel”: “self”, “href”: ” http://:/services/v2/installation/deployments “, “mediaType”: “application/json” }, { “rel”: “describedby”, “href”: ” http://:/services/v2/installation/deployments “, “mediaType”: “application/schema+json” } ], “messages”: [], “response”: { “$schema”: “ogg:installationDeployments”, “xagEnabled”: false, “deployments”: [ { “deploymentId”: “cf638afd-252e-4c79-ad55-fbeccb5d0434”, “deploymentName”: “Kafka”, “enabled”: true, “status”: “running” }, { “deploymentId”: “dc31de7c-39aa-4de2-a3df-5451e32ccef6”, “deploymentName”: “ServiceManager”, “enabled”: true, “status”: “running” } ] } } In this output from the CURL command, the “deploymentName” key will tell you the current deployment (s) installed on the host. In this example, both the ServiceManager and the Kafka deployment homes have to be migrated to the new Oracle GoldenGate (Microservices) Home. Patch ServiceManager via OGG_HOME To patch the ServiceManager deployment, organizations must update what Oracle GoldenGate (Microservices) Home the ServiceManager references. To do this, the following CURL command can be executed: curl -L -X PATCH ‘http://://services/v2/deployments/ServiceManager’ \ -H ‘Content-Type: application/json’ \ -H ‘Authorization: Basic b2dnYWRtaW46WFVNQEQzWXh1NWJpWmtGSQ==’ \ –data-raw ‘{ “oggHome”:”/app/orabd1″, “status”:”restart” }’ This CURL command feeds raw data that tells Oracle GoldenGate to update the deployment with an updated Oracle GoldenGate Home. Once the update has been completed, the ServiceManager will be restarted. After the restart, log in to the ServiceManager and confirm that the Oracle GoldenGate Home for the ServiceManager has been updated. Image 1 below shows what it would look like. Image 1: ServiceManger Updated Patching Deployment via OGG_HOME Any associated deployments can be migrated after the ServiceManager has been patched via migration to a new Oracle GoldenGate (Microservices) Home. The only requirement for a deployment to be migrated is that the ServiceManager has to be on the same or later version of Oracle GoldenGate. This means that organizations can have multiple Oracle GoldenGate (Microservices) Homes running simultaneously, depending on their deployments. In this example, all deployments must be migrated to the latest patch set (i.e., patched). To move a deployment, the process is precisely the same as was performed with the ServiceManager. To do this, the following CURL command can be executed: curl -L -X PATCH ‘http://:/services/v2/deployments/Kafka’ \ -H ‘Content-Type: application/json’ \ -H ‘Authorization: Basic b2dnYWRtaW46V0VsY29tZTEyMzQ1IyM=’ \ –data-raw ‘{ “oggHome”:”/app/orabd1″, “status”:”restart” }’ Once the deployment is restarted, all the Oracle GoldenGate processes (extract and replicat) will be upgraded to the latest versions of the binaries. To verify that the deployment was updated, check the ServiceManager and confirm that the Deployments section has an updated Oracle GoldenGate Home for the deployment. Image 2 below shows what this would look like. Image 2: Deployment Updated Summary Upgrading and Patching Oracle GoldenGate has always been an issue that takes time. In many cases, upgrading Oracle GoldenGate took six months to a year. Using Oracle GoldenGate (Microservices), an organization can quickly install, upgrade, or patch an environment with minimal downtimes. It is easing the time it takes to upgrade or repair, and the environment is critical as environments start to expand and data movement becomes key to organizational goals.", "image": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/051322_1310_PatchOracle1-1-2.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Bobby Curtis", "url": "https://rheodata.com/en-us/blog/author/bobby-curtis", "sameAs": "" }, "publisher": { "@type": "Organization", "name": "RheoData", "url": "rheodata.com", "logo": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" } } } ``` ```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" : "Datapump is used for Instantiating Oracle GoldenGate with an Initial Load. Client is facing performance issues with datapump for Index Organized Table (IOT) with approximately 1.9B rows. The root cause of the issue is source (DW) and target (OLTP) have different partition design for IOT. Source: PARTITION BY HASH and Target: PARTITION BY VALUES This is like trying to fit a square peg into a round hole. After several triages, import time was improved by more than 50%. Here are the details and triages. Database Version: 19.12.2.0.0 ------------------------------------------------------- ### This is the parameter file for export. ------------------------------------------------------- exclude=STATISTICS compression=ALL # Is it necessary to export staging? schemas=staging,s2,s3,s4,s5 flashback_scn=61727639035 # There are 8 CPUs and possible/typical to use 1.5-2.0 times the CPU and monitor. # Using more CPUs was never tested due to time constraints. # Recalled DBA was apprehensive to take risk in production. parallel=8 content=DATA_ONLY # Added for improvements to avoid 1 process doing all the work. # Tested using 1G/2G and no huge improvements. filesize=4G logfile=expdp.log dumpfile=schema%U.dmp directory=dpump_dir ------------------------------------------------------- ### This is the parameter file for import. ------------------------------------------------------- table_exists_action=TRUNCATE # Added for performance improvements since there is no standby database. transform=DISABLE_ARCHIVE_LOGGING:Y logtime=ALL metrics=Y # There are 8 CPUs and possible/typical to use 1.5-2.0 times the CPU and monitor. # Using more CPUs was never tested due to time constraints and risks. parallel=8 cluster=N schemas=staging,s2,s3,s4,s5 # STATISTICS was already exclude from import. # Exclude is not necessary, since objects were never exported. exclude=STATISTICS,REF_CONSTRAINT,GRANT,INDEX,TRIGGER content=DATA_ONLY logfile=impdp.log dumpfile=schema%U.dmp directory=dpump_dir ------------------------------------------------------- ### Here are the export dump files. ------------------------------------------------------- Dump file set for SYSTEM.SYS_EXPORT_SCHEMA_09 is: /export/schema01.dmp /export/schema02.dmp /export/schema03.dmp /export/schema04.dmp /export/schema05.dmp /export/schema06.dmp /export/schema07.dmp /export/schema08.dmp /export/schema09.dmp /export/schema10.dmp /export/schema11.dmp /export/schema12.dmp /export/schema13.dmp /export/schema14.dmp /export/schema15.dmp /export/schema16.dmp /export/schema17.dmp /export/schema18.dmp /export/schema19.dmp ------------------------------------------------------- ### Here is the import timing for IOT. ------------------------------------------------------- 16-MAR-23 20:25:48.971: W-4 . . imported H01 3.946 GB 474235896 rows in 12183 seconds using external_table 16-MAR-23 14:42:56.470: W-7 . . imported H02 3.952 GB 475007010 rows in 7113 seconds using external_table 16-MAR-23 23:45:07.344: W-8 . . imported H03 3.945 GB 474120834 rows in 11956 seconds using external_table 16-MAR-23 17:02:44.346: W-6 . . imported H04 3.949 GB 474655428 rows in 8386 seconds using external_table 16-MAR-23 23:58:02.815: Job SYSTEM.SYS_IMPORT_SCHEMA_02 completed at Thu Mar 16 23:58:02 2023 elapsed 0 11:18:20 There are probably more options to test and tune; however, there is time constraints to have imported completed. Futhermore, how much more gain can be achieved?", "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" : "GoldenGate Initial Load Using Datapump Import Performance Improvement for IOT with 1.9B rows", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/blog-feature-Logo-Oct-31-2025-04-57-26-6962-PM.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/goldengate-initial-load-using-datapump-import-performance-improvement-for-iot-with-1-9b-rows", "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" : "This week, I found myself deep in a familiar challenge – helping a client navigate the complexities of replicating Oracle Large Objects (LOBs) to Snowflake using Oracle GoldenGate. What started as a seemingly straightforward data integration project quickly revealed the nuanced technical considerations that separate successful migrations from frustrating dead ends. Let me give you the straight story about what we discovered and how we solved it. The Challenge: More Than Just Moving Data Our client came to us with what appeared to be a standard requirement: replicate Oracle CLOB data to Snowflake in real-time. Simple enough, right? Well, as anyone who’s worked with LOBs knows, there’s always more beneath the surface. The real challenge wasn’t just moving the data – it was understanding how Oracle stores LOBs and how that impacts replication to a completely different platform like Snowflake. Understanding the Foundation: What Are LOBs Anyway? Before we dive into the solution, let’s establish some common ground. In Oracle, LOBs (Large Objects) are data types designed to handle substantial amounts of character or binary data – think documents, images, or large text fields that exceed the limitations of standard VARCHAR2 columns. But here’s where it gets interesting: Oracle doesn’t store all LOBs the same way. Inline vs. Out-of-Line LOBs: The Critical Distinction Oracle uses two storage methods for LOBs, and understanding this distinction is crucial for successful replication: Inline LOBs: Small LOB data (typically under 4KB to 8KB, depending on your Oracle version) Stored directly within the table row alongside other column data Oracle GoldenGate can capture these changes directly from the redo logs More efficient for replication purposes Out-of-Line LOBs: LOB data exceeding the inline threshold Stored in separate LOB segments with only a pointer in the table row GoldenGate must fetch this data directly from the database Less performant for replication, especially with large LOBs The Oracle-to-Snowflake Translation Challenge Here’s where our client’s project got interesting. In Oracle, we’re dealing with CLOB data types. In Snowflake, these become VARCHAR columns. This isn’t just a simple rename – it’s a fundamental data type conversion that requires careful planning. Our client’s Oracle environment had CLOB columns that could theoretically hold massive amounts of data, but their business requirements kept most content under 15MB. Meanwhile, Snowflake VARCHAR columns can handle 64MB to 128MB (depending on documentation), giving us plenty of headroom. The challenge was ensuring GoldenGate could handle this conversion seamlessly. The Standard Setup: Getting the Basics Right Let me walk you through how we structured the tables to ensure compatibility. In Oracle, our standard table looked like this: CREATE TABLE CTMS_PSO.document_store (   id NUMBER GENERATED BY DEFAULT AS IDENTITY,   content CLOB NOT NULL CHECK (LENGTH(content) <= 15728640),   created_date DATE DEFAULT SYSDATE,   CONSTRAINT pk_document_store PRIMARY KEY (id) ); Optional: Create index on created_date for performance CREATE INDEX CTMS_PSO.idx_document_store_created ON document_store(created_date); Notice the check constraint limiting CLOB size to 15MB – this business rule became crucial for our Snowflake design. The corresponding Snowflake table: CREATE TABLE ctms_pso.document_store (   id NUMBER AUTOINCREMENT,   content VARCHAR(16777216) NOT NULL,   created_date TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),   CONSTRAINT pk_document_store PRIMARY KEY (id) ); The VARCHAR(16777216) gives us 16MB capacity – slightly larger than our Oracle constraint to provide a safety buffer. The GoldenGate Configuration: Where the Magic Happens Here’s where our experience really paid off. Oracle GoldenGate handles LOB replication differently depending on your target system: 1. Oracle-to-Oracle: LOBs replicate in pieces (partial LOB replication) 2. Oracle-to-Non-Oracle: You need the complete LOB for each transaction For our Snowflake target, we needed to ensure complete LOB capture. The key parameter in our Extract configuration: EXTRACT ETSCSF3 USERIDALIAS SOURCE DOMAIN OracleGoldenGate EXTTRAIL WW REPORTCOUNT EVERY 2 MINUTES, RATE WARNLONGTRANS 30MIN CHECKINTERVAL 10MIN TRANLOGOPTIONS INTEGRATEDPARAMS (MAX_SGA_SIZE 512, PARALLELISM 2) TRANLOGOPTIONS FETCHPARTIALLOB NOCOMPRESSUPDATES TABLE FREEPDB1.CTMS_PSO.document_store; The `TRANLOGOPTIONS FETCHPARTIALLOB` parameter is your best friend here. When Extract receives partial LOB content from the logmining server, this forces it to fetch the complete LOB image instead of just processing the partial content. The Replicat configuration remained straightforward: REPLICAT REPSF REPERROR(DEFAULT, ABEND) REPORTCOUNT EVERY 1 MINUTES, RATE GROUPTRANSOPS 10000 MAXTRANSOPS 20000 MAP FREEPDB1.CTMS_PSO.document_store, TARGET TRACTORSUPPLY.CTMS_PSO.document_store; The Plot Twist: When 15MB Becomes 8MB Just when we thought we had everything figured out, our client threw us a curveball. Due to their existing Snowflake table structure and constraints from a previous replication tool, they needed to limit all LOBs to exactly 8MB during replication. This required a more sophisticated approach using GoldenGate’s SQLEXEC functionality. SQLEXEC: The Swiss Army Knife of GoldenGate SQLEXEC allows GoldenGate to execute database commands within the replication process. Think of it as a way to transform data on-the-fly during extraction. Here’s how we modified the Extract to capture only the first 8MB of each LOB: EXTRACT ETSCSF3 USERIDALIAS SOURCE_TSC DOMAIN OracleGoldenGate EXTTRAIL WW REPORTCOUNT EVERY 2 MINUTES, RATE WARNLONGTRANS 30MIN CHECKINTERVAL 10MIN TRANLOGOPTIONS INTEGRATEDPARAMS (MAX_SGA_SIZE 512, PARALLELISM 2) TRANLOGOPTIONS FETCHPARTIALLOB NOCOMPRESSUPDATES TABLE FREEPDB1.CTMS_PSO.document_store, SQLEXEC(ID lob_id, QUERY select dbms_lob.SUBSTR(content, 1, 8388608) from CTMS_PSO.document_store where ID = :LOB_ID, PARAMS(LOB_ID = ID), EXEC SOURCEROW); Let me break down this SQLEXEC command: ID lob_id – Creates a parameter variable QUERY “select dbms_lob.SUBSTR(content, 1, 8388608)…” – Executes a substring operation capturing exactly 8MB (8388608 bytes) PARAMS(LOB_ID = ID) – Maps the table’s ID column to our parameter EXEC SOURCEROW); – Runs this SQL for every captured row The beauty of this approach is that it handles all DML operations – inserts, updates, and deletes – automatically applying the 8MB limit during extraction. The Results: Mission Accomplished After implementing this solution, our client achieved exactly what they needed: Real-time replication of Oracle CLOBs to Snowflake VARCHARs Automatic truncation to 8MB to match their existing architecture Reliable, consistent performance across all transaction types Clean integration with their existing Snowflake environment Key Takeaways for Your Oracle-to-Snowflake Journey Understand your LOB storage patterns – inline vs. out-of-line makes a significant difference in replication performance Plan your data type mapping carefully – Oracle CLOBs to Snowflake VARCHARs requires thoughtful sizing Use FETCHPARTIALLOB for non-Oracle targets – this ensures complete LOB capture in your trail files Leverage SQLEXEC for data transformation – when you need to modify data during extraction, this is your tool Test thoroughly with realistic data volumes – LOB replication behaves differently under various load conditions Partner with the Experts Data integration projects like Oracle-to-Snowflake migrations involve countless technical nuances that can make or break your success. At RheoData, we’ve navigated these challenges across dozens of enterprise implementations, combining deep Oracle expertise with modern cloud platform knowledge. Whether you’re planning a complete migration or need to solve specific replication challenges, our team brings the experience and proven methodologies to ensure your data integration project succeeds. Ready to tackle your Oracle-to-Snowflake integration challenge? Let’s coordinate on a solution that fits your specific requirements. Contact RheoData (cloud@rheodata.com) today to discuss how we can accelerate your data transformation journey.", "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" : "When Your Oracle LOBs Won’t Play Nice with Snowflake: A Real-World Solution", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1269579935-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-lob-replication-snowflake-goldengate-solution", "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" : "“Our Oracle databases are drowning in decades of business data, and our analysts are spending more time waiting for reports than analyzing them. We need to get this data into Snowflake for real-time analytics, but we can’t afford any disruption to our production systems.” Sound familiar? This conversation happens in our office at least twice a month. IT leaders are caught between the pressure to modernize analytics capabilities and the reality that their Oracle databases are mission-critical systems that simply cannot fail. The good news? Oracle GoldenGate provides a proven path to replicate your Oracle data to Snowflake in real-time without touching your production workloads. RheoData has helped companies achieve 99.9% uptime during these migrations while reducing query response times by up to 78%. Let us walk you through exactly how this works—and more importantly, how to avoid the costly mistakes we’ve seen derail similar projects. Why Oracle GoldenGate for Snowflake Integration? Before diving into the technical details, let’s address the elephant in the room: why not just use batch ETL processes or direct database links? In enterprise environments, we’ve seen three critical requirements that eliminate simpler approaches: Zero Production Impact: Your ERP systems, CRM platforms, and operational databases cannot experience performance degradation Near Real-Time Analytics: Business decisions need current data, not yesterday’s batch job results Minimal Downtime Windows: Business operations don’t accommodate lengthy maintenance windows Oracle GoldenGate addresses all three by capturing changes from Oracle transaction logs without impacting source system performance, then streaming those changes to Snowflake in near real-time. Architecture Overview The architecture consists of four main components: Source Oracle Database: Your existing production systems remain untouched Oracle GoldenGate Hub: Captures and processes change data Target Snowflake Environment: Your analytics destination Monitoring & Management Layer: Ensures data integrity and performance This hub-and-spoke model means you can replicate from multiple Oracle sources to Snowflake simultaneously—critical for organizations with distributed database environments. Prerequisites and Planning Technical Requirements Source Oracle Environment: Oracle Database 11.2.0.4 or higher Archive log mode enabled Sufficient archive log retention (minimum 24 hours recommended) GoldenGate supplemental logging configured Dedicated database user with appropriate privileges Target Snowflake Environment: Active Snowflake account with appropriate compute resources Database, schema, and warehouse pre-configured Staging area for initial data loads Proper user roles and security permissions established GoldenGate Infrastructure: Dedicated GoldenGate server (physical or virtual) Network connectivity between all components Sufficient storage for trail files (plan for 2-3 days retention minimum) Monitoring tools and alerting capabilities Critical Planning Considerations Based on our experience with enterprise clients, these planning steps are non-negotiable: 1. Change Data Volume Assessment Analyze transaction log generation patterns over time Identify peak processing periods and data volumes Calculate network bandwidth requirements for replication traffic Plan for growth in data volumes over the next 12-24 months 2. Network Infrastructure Validation Ensure network can handle peak replication loads plus 30% overhead Test connectivity during business hours under normal load conditions Implement network monitoring to track bandwidth utilization Configure appropriate firewall rules and security protocols 3. Downtime Window Planning While GoldenGate minimizes downtime, initial setup requires brief outages Coordinate with business stakeholders for optimal timing Plan rollback procedures in case of implementation issues Communicate timeline expectations to all affected teams Step-by-Step Implementation Guide Phase 1: Oracle Source Configuration Oracle Database Preparation: Verify database is running in ARCHIVELOG mode (required for change data capture) Enable database-level supplemental logging to capture complete change information Configure table-level supplemental logging for specific business tables Ensure sufficient archive log retention (minimum 24 hours, recommend 72 hours) Test archive log generation during peak business periods GoldenGate User Setup: Create dedicated Oracle user account for GoldenGate operations Grant necessary privileges including CONNECT, RESOURCE, SELECT ANY DICTIONARY Provide FLASHBACK privileges for consistent read operations Configure table-level permissions for source business schemas Test connectivity and permissions before proceeding Phase 2: GoldenGate Infrastructure Setup GoldenGate Installation: Install Oracle GoldenGate software on dedicated server infrastructure Create required directory structure for trail files, parameter files, and reports Configure network connectivity between Oracle source and GoldenGate server Validate sufficient storage space for trail file retention requirements Set up monitoring and alerting for disk space utilization Deployment Configuration: Configure GoldenGate Service Manager and associated deployment services (5 ports (first deployment)) Enable automatic restart capabilities for extract processes Configure trail file purging based on checkpoint advancement Establish lag reporting thresholds for monitoring and alerting Extract Process Setup: Create and configure primary extract process to capture Oracle changes Define source table specifications for business data tables Configure remote trail file destination pointing to replication target Set up DDL replication for schema change propagation Enable extract process and validate initial trail file generation Phase 3: Snowflake Target Environment Snowflake Infrastructure Preparation: Create target database and schema structure in Snowflake environment Provision appropriately sized virtual warehouse for replication workload Configure auto-suspend and auto-resume settings for cost optimization Set up staging areas for initial data load operations Create target table structures matching Oracle source schema Connectivity and Security: Install and configure Snowflake connector for GoldenGate integration Set up secure connection parameters including authentication credentials Configure network access rules and firewall exceptions as needed Test connectivity between GoldenGate server and Snowflake environment Validate target table accessibility and write permissions Phase 4: Replication Process Configuration Replicat Process Setup: Create and configure replicat process for Snowflake target delivery Map source Oracle tables to corresponding Snowflake target tables Configure batch processing parameters for optimal performance Set up error handling and conflict resolution strategies Enable replicat process and validate initial data delivery Performance Optimization: Configure transaction grouping for improved throughput Set appropriate batch sizes based on network and target capacity Enable parallel processing where supported by target environment Configure checkpoint intervals for recovery and restart capabilities Implement monitoring for replication lag and throughput metrics Initial Data Load Strategy For large enterprise datasets, initial loads require careful orchestration: Planning the Initial Load: Identify tables requiring initial synchronization Determine optimal load order based on dependencies Plan for large table partitioning during load process Schedule loads during low-activity periods Prepare rollback procedures for failed loads Load Execution Process: Export data from Oracle using appropriate tools Transfer data securely to Snowflake staging areas Execute bulk loads using Snowflake’s COPY commands Validate data integrity and completeness Synchronize change capture from specific SCN points Post-Load Validation: Compare row counts between source and target systems Validate key business metrics and data relationships Test query performance on newly loaded data Confirm real-time replication is functioning correctly Update documentation and runbooks Monitoring and Maintenance Key Performance Metrics Monitor these critical metrics to ensure optimal performance: Replication Health Indicators: Extract lag times (target: less than 5 minutes during normal operations) Replicat processing throughput and error rates Trail file disk usage and purging effectiveness Network bandwidth utilization for replication traffic Snowflake Performance Metrics: Query response times compared to baseline performance Warehouse utilization and auto-scaling effectiveness Storage costs and data growth patterns User adoption and analytics usage patterns System Resource Monitoring: GoldenGate server CPU, memory, and disk utilization Oracle database performance impact (should be minimal) Network latency and packet loss between components Error rates and automatic recovery success rates Automated Monitoring Setup Alert Configuration: Set up automated alerts for replication lag exceeding thresholds Monitor disk space on GoldenGate servers with appropriate warnings Configure notifications for process failures or abends Implement health checks for connectivity between all components Performance Dashboards: Create real-time dashboards showing replication status Track business-critical data freshness metrics Monitor cost optimization opportunities in Snowflake Provide visibility into system performance for stakeholders Best Practices for Enterprise Environments 1. Handle Business Schedule Dependencies Business operations have specific timing requirements. Plan accordingly: Batch Processing Optimization: Configure GoldenGate to handle large batch updates efficiently Optimize replication during end-of-period processing Plan for month-end, quarter-end processing spikes Coordinate with business users for planned maintenance 2. Implement Data Quality Assurance Continuous Data Validation: Set up automated data quality checks between source and target Implement row count comparisons and key metric validations Create alerts for data discrepancies exceeding thresholds Establish procedures for investigating and resolving data issues 3. Security and Compliance Data Protection Measures: Encrypt data in transit between all system components Implement proper access controls and user authentication Maintain audit trails for all replication activities Ensure compliance with relevant data protection regulations Performance Optimization Snowflake Warehouse Sizing Right-size your Snowflake infrastructure based on actual usage: Capacity Planning: Start with medium-sized warehouses and monitor utilization Enable multi-cluster scaling for concurrent user access Configure auto-suspend settings to optimize costs Monitor query performance and adjust sizing as needed Cost Optimization: Track warehouse usage patterns and optimize schedules Implement appropriate data retention and archiving policies Use resource monitors to control unexpected cost spikes Regular review and adjustment of warehouse configurations GoldenGate Performance Tuning Infrastructure Optimization: Configure extract processes for optimal throughput Implement parallel processing where appropriate Optimize trail file management and purging Monitor and tune network configuration parameters Process Configuration: Set appropriate batch sizes for target system capacity Configure transaction grouping for improved efficiency Implement checkpoint intervals for optimal recovery Monitor and adjust based on actual performance metrics Measuring Success Track these KPIs to validate your implementation: Technical Success Metrics: Replication lag consistently under 5 minutes during normal operations Data accuracy rate of 99.99% or higher between source and target System availability of 99.9% uptime or better Zero impact on source Oracle database performance Business Value Metrics: Query response time improvement of 60-80% compared to legacy systems Report generation time reduction of 70-90% for standard reports Increased analyst productivity measured by time-to-insight improvements Cost savings from infrastructure optimization and improved efficiency User Adoption Indicators: Number of active users accessing real-time analytics Frequency of data requests and self-service analytics usage Reduction in IT support tickets related to data access Business stakeholder satisfaction with data freshness and accessibility Your Next Steps: From Planning to Production Success We’ve walked through the technical implementation, but here’s what RheoData has learned from helping IT leaders navigate this transformation: the technology is only half the battle. The real challenges lie in managing stakeholder expectations, coordinating with business schedules, and ensuring your team has the expertise to maintain these systems long-term. We’ve seen perfectly architected solutions fail because of inadequate change management, and we’ve seen imperfect implementations succeed because the team understood the business context. The questions you should be asking yourself right now: Do you have the internal expertise to handle the inevitable 2 AM support calls? Have you planned for the hidden complexities of your specific Oracle configurations? Is your team prepared to optimize Snowflake costs as data volumes grow? What happens when your key personnel leave during the implementation? Why RheoData Can Accelerate Your Success Over the past five years, RheoData has guided companies through exactly this type of transformation. Our clients don’t just get technical implementation—they get a partner who understands that database downtime affects business operations, that integration projects must account for real-world constraints, and that every configuration decision must balance performance, cost, and maintainability. What makes RheoData’s approach different: Real-World Expertise: We understand the practical challenges of enterprise database environments Risk-First Implementation: Every step planned around minimizing business disruption Knowledge Transfer Focus: Your team becomes self-sufficient, not dependent on outside consultants Transparent Methodology: Clear roadmaps, realistic timelines, no hidden costs or unrealistic promises Recent RheoData client results that matter: 78% reduction in query response times for a Fortune 500 company Zero production downtime during migration for a critical business system $200K annual cost savings through Snowflake optimization 6-month ROI achieved through improved analyst productivity Ready to Start Your Oracle-to-Snowflake Journey? If you’re facing pressure to modernize your analytics capabilities while maintaining rock-solid production systems, let’s have a conversation. RheoData offers a complimentary 15-minute assessment call where we’ll discuss: Your specific Oracle environment and replication requirements Timeline constraints and business priorities Risk mitigation strategies for your organization Realistic cost and resource expectations No sales pitch, no generic recommendations—just honest expertise from a team that’s helped companies navigate exactly where you are now. Schedule your complimentary assessment call (678)-608-1352 or email cloud@rheodata.com directly. Because when your business depends on data, you need a partner who understands that technology decisions are really about people, processes, and the confidence to sleep well knowing your systems will work when it matters most. RheoData specializes in database transformations for enterprise organizations. With over 15 years of combined experience in mission-critical Oracle environments, RheoData has helped dozens of companies successfully migrate to cloud analytics platforms while maintaining 99.9%+ uptime.", "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 to Snowflake: Your Complete Guide to Real-Time Data Integration with Oracle GoldenGate", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/snowflake-complete-guide-scaled-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-to-snowflake-exec-complete-guide", "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", "headline": "Silent install for Oracle GoldenGate 21c (Big Data – Microservices edition) – Step 1 of 2", "mainEntityOfPage": "https://rheodata.com/en-us/blog/silent-install-for-oracle-goldengate-21c-big-data-microservices-edition-step-1-of-2", "datePublished": "10/11/2025", "dateModified": "10/11/2025", "articleBody": "Been awhile since I did a post on installing Oracle GoldenGate. The last post I did was back in 2019 which showed how to do a silent install of Oracle GoldenGate 12c – Classic. In looking through my posts I realized I have not done one on installing Microservices (any of them) silently; here is a post on installing Oracle GoldenGate 21c for Big Data (Microservices). Note: This same approach can be followed with all the Oracle GoldenGate Microservices releases (12.3.0.0.1 or later). Downloading Binaries The binaries for Oracle GoldenGate for Big Data are downloaded from the Oracle Technology Network (OTN). The current published release is 21.4.0.0.0. However, as of April 26, 2022, there have been announcements that 21.5.0.0.0 has been released (it has not appeared on any Oracle sites yet). Download the version which you need. Unzip Binaries After downloading the binaries, they need to be copied to the server in which Oracle GoldenGate will be run. Once the binaries are transferred, they need to be unzipped to a temporary directory $ unzip ./214000_ggs_Linux_x64_BigData_services_shiphome.zip -d ./ogg21cbd Create Directories Before installing Oracle GoldenGate for Big Data, establishing the directory structure needs to be done. The following commands can be used to create the Oracle GoldenGate Home and other directories needed: $ mkdir -p /opt/app/oracle/product/21.4.0/ogghome_1 $ mkdir -p /opt/app/oraInventory Installing Oracle GoldenGate With the binaries unzipped, the next step is to install Oracle GoldenGate. This process will be done using the silent install process. To perform a silent, install the oggcore.rsp file needs to be updated. The information below is a sample response file that illustrates what a working file looks like without the comments. See in mind that your file will look a bit different depending on you $OGG_HOME AND Inventory location Example: oracle.install.responseFileVersion=/oracle/install/rspfmt_ogginstall_response_schema_v21_1_0 INSTALL_OPTION=Generic SOFTWARE_LOCATION=/opt/app/oracle/product/21.4.0/ogghome_1 INVENTORY_LOCATION=/opt/app/oraInventory UNIX_GROUP_NAME=oinstall Once a response file has been defined, the next thing is to install Oracle GoldenGate with the response file: $ cd ./ogg21cbd/ggs_Linux_x64_BigData_services_shiphome/Disk1 $ ./runInstaller -silent \ -ignoreSysPrereqs \ -ignorePrereq \ -showProgress \ -waitForCompletion \ -responseFile \ /tmp/ogg21cbd/ggs_Linux_x64_BigData_services_shiphome/Disk1/response/oggcore.rsp After the installation is done, make sure that the orainstRoot.sh file is run as the root user. Once the binaries are installed, your Oracle GoldenGate Home ($OGG_HOME) is now defined. Update the needed profile information (.bash_profile or .bashrc) to have this defined automatically when you login to the user. The next step is to define and install the deployment home ($DEPLOYMENT_HOEM) that will be used within the architecture. I’ll write this up in the next post on this topic. Enjoy!!", "image": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/blog-feature-Logo-Oct-31-2025-04-57-26-6962-PM.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Bobby Curtis", "url": "https://rheodata.com/en-us/blog/author/bobby-curtis", "sameAs": "" }, "publisher": { "@type": "Organization", "name": "RheoData", "url": "rheodata.com", "logo": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" } } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "With the introduction of Oracle GoldenGate (Microservices), Oracle opened a lot of doors for people and organizations to create tools based off of the REST APIs. Initially, the REST APIs within Oracle GoldenGate were developed to ease administration and configuration of the product. This has allowed, RheoData to extend, build, and introduce GoldenGate Service Monitor. Overview The “GoldenGate Services (GGS) Monitor” is a Python script designed to monitor the status and lag of Oracle GoldenGate Extract and Replicat processes. It interacts with the GoldenGate Management REST API to fetch and display information about these processes, such as their status, lag, position, and last start time. The script presents this information in a tabular format on the terminal screen. Prerequisites 1. Python 3.x installed on your local machine. 2. The required Python packages should be installed. You can install them using `pip`: pip install requests json dotenv Running the Script To run the “GoldenGate Services (GGS) Monitor” script on your local machine, follow these steps: 1. Clone the Repository: Download the zip file from https://rheodata.com 2. Configure Environment Variables: Update the ./bin/.env file with the following information URL= AUTH= (https://www.base64encode.org/) REFRESH= 3. Execute the Script: Open a terminal window and navigate to the directory where the script is located. Run the script using the following command: python3 ./bin/ggsmon.py 4. Monitor the Output: The script will start running and display a tabular output on the terminal screen. The output will include information about Extract and Replicat processes, including their status, lag, position, and last start time. The information will be refreshed based on the interval specified in the `REFRESH` environment variable. Notes The script uses multithreading to fetch and process information in parallel, improving performance. It handles exceptions, such as missing data or keyboard interrupts, to provide meaningful output. The script’s main loop continuously refreshes the screen to display updated information. Make sure to keep your environment variables, especially the `AUTH` token, secure and confidential. License This script is released under the MIT License Agreement. Please refer to the LICENSE file included with the package for more details. Contact Information If you have any questions or need assistance, feel free to contact RheoData at hello@rheodata.com.", "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" : "GoldenGate Service Monitor 0.1.0", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/ggsmon_blog_image-1024x644-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/goldengate-service-monitor-0-1-0", "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" } } ```