--- title: RheoData Blog | Cloud (7) description: Cloud | RheoData Blog Posts (7) --- [![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) - [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) [![](https://rheodata.com/hs-fs/hubfs/Imported_Blog_Media/blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png?width=100&height=100&name=blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png)](https://rheodata.com/) [![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) - [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) [![](https://rheodata.com/hs-fs/hubfs/Imported_Blog_Media/blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png?width=100&height=100&name=blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png)](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) - [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 # Cloud (7) ## [Using NGINX in unsecure mode for Oracle GoldenGate](https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:27 PM Sometimes, you just want to run NGINX in unsecure mode (over port 80) for internal environments.... [CONTINUE READING](https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate) ## [Oracle@GCP Setup: Enterprise Database Power Made Simple](https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:26 PM [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment) ## [Manually purging trail files from OCI GoldenGate Service](https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:24 PM Oracle GoldenGate Service is Oracle’s cloud offering to quickly use GoldenGate to move data within... [CONTINUE READING](https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service) ## [MariaDB to MySQL 8.0/Heatwave Migrations](https://rheodata.com/en-us/blog/migrate-mariadb-to-mysql) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:22 PM Due to financial struggles, MariaDB lost over 87% of its Initial Public Offering (IPO) after going... [CONTINUE READING](https://rheodata.com/en-us/blog/migrate-mariadb-to-mysql) ## [Writing Python documentation with Cohere’s Generative AI](https://rheodata.com/en-us/blog/python-script-doc-cohere) Posted by [rheostage5132](https://rheodata.com/en-us/blog/author/rheostage5132) | Nov 10, 2025 9:29:21 PM Occasionally, I will write a script that customers can use. When I do this, they often ask for... [CONTINUE READING](https://rheodata.com/en-us/blog/python-script-doc-cohere) ## [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) ## [Similarity Search with Oracle’s Vector Datatype, Python, and Flask](https://rheodata.com/en-us/blog/similarity-search-python-flask) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:18 PM [CONTINUE READING](https://rheodata.com/en-us/blog/similarity-search-python-flask) ## [Oracle Vector Datatype – Updating table data](https://rheodata.com/en-us/blog/oracle-vector-datatype-updating-table-data) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:15 PM [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-vector-datatype-updating-table-data) ## [HashiCorp Vault Health Check … Is your HashiCorp Vault healthy?](https://rheodata.com/en-us/blog/hashicorp-vault-health-check-get-a-checkup-today) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:13 PM HashiCorp is constantly improving their HashiCorp Vault Open Source System (OSS) product and... [CONTINUE READING](https://rheodata.com/en-us/blog/hashicorp-vault-health-check-get-a-checkup-today) ## [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) ### 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" : "Cloud | RheoData Blog Posts (7)", "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/cloud/page/7", "@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" : "Sometimes, you just want to run NGINX in unsecure mode (over port 80) for internal environments. The steps here are similar to what I posted some time ago in this post -> here. I’ll put these steps here as well and highlight (bold) which steps were different. 1. SSH into the RedHat instance $ ssh @ 2. Sudo to Root $ sudo su – 3. Install and confirm installation of Nginx $ dnf -y install nginx && dnf list install nginx After installing NGINX, the next thing to do is to configure it against Oracle GoldenGate. Configure NGINX 1. Go to the Reverse Proxy directory under $OGG_HOME $ cd $OGG_HOME/lib/utl/reverseproxy 2. Run ReverseProxySettings with options (NO SSL) $ ./ReverseProxySettings -u oggadmin -P —-no-ssl -o ogg.conf http://localhost: 3. Copy the config file to NGINX directory $ sudo cp ogg.conf /etc/nginx/conf.d/nginx.conf 4. Remove or rename the default configuration file $ cd /etc/nginx/conf.d $ mv ./default.conf ./_default.con_ 5. Start NGINX $ sudo nginx & 6. Test NGINX config $ sudo nginx -t 7. Reload NGINX $ sudo nginx -s reload 8. Access ServiceManager and other services without port numbers Open a web browser and navigate to the URL, something similar to this:  http://:80 End Result With NGINX configured, you can now access Oracle GoldenGate on port 80. In the example below, we are using a SecureLink connection and the port numbers are off, but the last two digits are the important ones. They show that we are using Port 80 for the connection.", "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" : "Using NGINX in unsecure mode for Oracle GoldenGate", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/Shutterstock_657322222-3.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate", "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" : "Let me give you the straight story: setting up Oracle Database on Google Cloud Platform used to require weeks of planning, coordination between multiple teams, and enough documentation to fill a small library. Those days are behind us. What you’re about to see is how Oracle@GCP transforms enterprise database deployment into a streamlined process that any database administrator can execute confidently. Why Oracle@GCP Changes Everything Your expertise is invaluable when it comes to database strategy, but you shouldn’t have to spend weeks wrestling with infrastructure complexity. Oracle@GCP delivers the full power of Oracle Database Enterprise Edition with the operational simplicity of Google Cloud’s managed services. Here’s exactly how simple the setup process has become. Step-by-Step Setup: From Search to Success Step 1: Find Oracle Database Services Starting from your Google Cloud Console, simply search for “oracle” in the top search bar. The platform immediately surfaces Oracle Database@Google Cloud as your first option, making discovery effortless and eliminating any guesswork about service availability. Step 2: Choose Your Oracle Solution Google Cloud presents you with clear options for Oracle database deployment. Select “Autonomous Database” to access Oracle’s self-managing database service, which handles routine maintenance tasks automatically while you focus on strategic initiatives. Click “Explore service” under the Autonomous Database option to access the service dashboard. The interface immediately shows you the autonomous database management area where you’ll create and monitor your Oracle instances. Step 3: Navigate to Autonomous Database The Autonomous Database dashboard displays your current instances (if any) and provides a prominent “Create” button for new deployments. This clean interface eliminates complexity while giving you full visibility into your database inventory. Click the “Create” button to launch the database creation wizard. The system guides you through a logical sequence of configuration decisions, ensuring you don’t miss critical settings while maintaining deployment speed. Step 4: Configure Instance Details Enter your Instance ID, Database name, and Display name using your organization’s naming conventions. The system validates your entries in real-time and shows you exactly which fields are permanent versus modifiable later, preventing costly mistakes. Step 5: Select Workload Type Choose from four optimized workload configurations: Data Warehouse, Transaction Processing, JSON, or APEX. Each option is clearly explained with use cases, allowing you to select the configuration that matches your specific performance requirements without extensive research. Step 6: Configure Database Specifications Set your license type (BYOL or new), Oracle Database edition, version, CPU count, and storage requirements. The interface provides clear guidance on scaling options and shows cost implications in real-time, enabling informed decision-making. Step 7: Set Backup Retention Configure your backup retention period from 1-60 days based on your compliance and recovery requirements. Oracle manages the entire backup process automatically, eliminating the operational overhead of traditional backup management. Step 8: Establish Administrator Credentials Create your ADMIN username and secure password for database administration. The system enforces Oracle’s security standards while keeping the credential setup process straightforward and secure. Step 9: Configure Network Access Select your network access model: secure access from everywhere, IP-restricted access, or private endpoint access only. The default secure access option provides immediate connectivity while maintaining enterprise-grade security through database credentials and connection wallets. Step 10: Set Operational Contacts Add notification email addresses for operational updates and announcements. The system keeps you informed of maintenance windows, updates, and any issues without overwhelming your inbox with unnecessary alerts. Step 11: Complete Database Creation Click “Create” to deploy your Oracle Autonomous Database. The system begins provisioning immediately, with typical deployment times measured in minutes rather than hours or days. Real-Time Deployment Monitoring Monitoring Phase 1: Initial Provisioning Your database appears in the dashboard with “Provisioning (0%)” status immediately after creation starts. The real-time status updates keep you informed of deployment progress without requiring constant manual checking. Monitoring Phase 2: Active and Ready Once provisioning completes, your database status changes to “Available” with full resource allocation displayed (2 ECPU, 1 TB storage). The system provides immediate confirmation that your database is ready for connections and workload deployment. Seamless OCI Integration Direct OCI Access Notice the “Manage in OCI” button prominently displayed in your Google Cloud console. This direct integration allows you to leverage Oracle’s native management tools without losing the benefits of Google Cloud’s infrastructure and billing integration. Full OCI Administrative Control Clicking “Manage in OCI” provides immediate access to comprehensive database management within Oracle Cloud Infrastructure. You gain access to advanced configuration options, detailed monitoring, disaster recovery settings, and all enterprise-grade administrative capabilities you expect from Oracle Database. What This Means for Your Organization The setup process you just witnessed typically completes in under 15 minutes from start to finish. Compare that to traditional Oracle database deployments that require infrastructure procurement, OS installation, Oracle software installation, network configuration, security hardening, and backup setup – processes that often take weeks to coordinate and execute. Your team gets enterprise-grade Oracle Database functionality with cloud-native operational simplicity. No compromise on database capabilities, no sacrifice of security or performance standards, and no extended deployment timelines that delay critical business initiatives. Ready to Transform Your Database Strategy? Oracle@GCP delivers exactly what you need: proven Oracle Database technology with Google Cloud operational excellence. The deployment process is this straightforward, the management is this intuitive, and the results are this reliable. Let’s coordinate on your Oracle@GCP implementation. Your database infrastructure should accelerate your business objectives, not slow them down. Ready to get started? Contact RheoData (cloud@rheodata.com) today to discuss how Oracle@GCP fits your specific requirements. We’ll help you plan the migration, execute the deployment, and optimize your database performance from day one.", "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@GCP Setup: Enterprise Database Power Made Simple", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_618169402-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment", "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": "Manually purging trail files from OCI GoldenGate Service", "mainEntityOfPage": "https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service", "datePublished": "10/11/2025", "dateModified": "10/11/2025", "articleBody": "Oracle GoldenGate Service is Oracle’s cloud offering to quickly use GoldenGate to move data within OCI as well as other clouds. Now network connection and bandwidth has a bit to do with the speed of data being processed, but it a quick service over all. One thing that any GoldenGate Administrator has to get use to is the lack of access to the underlying host where GoldenGate is running. As my friends, the PMs, have told me this is due to GoldenGate Service being a “SERVICE”. This basically means you do not need or will get access to the underlying filesystem of GGS. For many GoldenGate Administrators this will be frustrating from a troubleshooting aspect – How do you confirm or make sure that trail files are being written to or read from? Well, the answer is in OCI GoldenGate Service, but that is not the point of this post. The item that needs to be discussed is how to clean up trail files in GGS? Cleaning up trail files is important because they do take space and if you don’t have a task enabled to clean up trail files, then space will be consumed and eventually used up. How do you take care of this issue then manually? The answer is simple and what is built into Oracle GoldenGate and Oracle GoldenGate Service – REST APIs. To purge a single set of trail files (all trail files) that begin with a specific name, the below code can be used in Microsoft Visual Studio Code (VSCode). @url = ### POST /services/v2/commands/execute Authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE Content-Type: text/plain { name: purge, purgeType: trails, trails: [ { name: “AL } ], useCheckpoints: false, keep: [ { type: min”, units: files”, value: 0 } ] } ### In the above example code, we are removing all the trail files that being with “AL”. If you want to remove more than one series of trail files, we can simply add more trail file names to the code as follows: @url = ### POST /services/v2/commands/execute Authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE Content-Type: text/plain { name: purge, purgeType: trails, trails: [              {                 name: “AL”               },               {                  “name”:”AB”               } ], useCheckpoints: false, keep: [                {                   type: min”,                    units: files”,                    value: 0                }          ] } ### If you are curious how this would look in a written cURL command, the below would do the same thing: curl --request POST \ --url /services/v2/commands/execute \ --header 'authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE' \ --header 'content-type: text/plain' \ --header 'user-agent: vscode-restclient' \ --data '{name: purge,purgeType: trails,trails: [{name: AL}],useCheckpoints: false,keep: [{type: min,units: files,value: 0}]}' Enjoy!", "image": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/curlogg-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" : "Due to financial struggles, MariaDB lost over 87% of its Initial Public Offering (IPO) after going public in December 2022. During the first day of trading in 2022, MariaDB’s stock declined 40%. On the second day, the stock dropped an additional 20%. Leaving the company’s future uncertain and forcing MariaDB to seek more funding to continue operations. With the uncertainness of MariaDB, customers are facing an uncertain future: How soon will MariaDB run out of money? Who will fix bugs and vulnerabilities if MariaDB no longer exists? How can customers justify running their database infrastructure on a product that may no longer be supported or developed? The simple answer is to migrate to MySQL 8.0/Heatwave on Oracle Cloud Infrastructure (OCI)! MySQL is the upstream open-source product in the MySQL ecosystem and is the natural, low-risk replacement for MariaDB. MySQL is the world’s most popular open-source database and has the financial backing of Oracle. Migrating from MariaDB to MySQL Heatwave MariaDB has significantly diverged and is no longer a drop-in compatible product to MySQL. Organizations have two strategies that can be used to migrate from MariaDB to MySQL 8.0/Heatwave: A logical dump and load to MySQL 8.0/Heatwave A near-zero downtime migration using migration tools (Oracle GoldenGate, Five-Tran, Qlik, etc.) Logical migration process To logically move from MariaDB to MySQL 8.0/Heatwave, the process consists of four operations: Evaluate MariaDB for incompatibilities Dump the data (logical) Build an OCI MySQL 8.0/Heatwave instance Load the data Near-Zero Migration Process MariaDB to MySQL 8.0/Heatwave can use Change Data Capture software to logically migrate between databases to ensure continuous business operations. The process consists of eight operations: Evaluate Maria DB for incompatibilities Establish CDC capture process Dump the data (logical) Build an OCI MySQL 8.0/Heatwave instance Load the data Establish CDC apply process Sync all data Switch over application Eventual Incompatibles In both migration processes, the evaluation of MariaDB may be the most challenging. The longer you wait to start the migration, the longer the process will become. The following areas need to be evaluated: High Availability In MariaDB, Galera, a plugin developed by a 3rd party company, Cordership, provides high availability. This creates additional uncertainty and risk for MariaDB users. MySQL includes native, built-in, HA, and DR using Group Replication, InnoDB Cluster, ClusterSet, and ReplicaSet. MySQL high availability is 100% developed, maintained, and supported by the MySQL Team at Oracle. Storage Engines MariaDB Community Edition contains various storage engines that are not included in MariaDB Enterprise Edition. InnoDB is the main engine used for transaction processing, and before migrating to MySQL 8.0/Heatwave, you will need to convert data in the other storage engines to InnoDB. Functions MariaDB has functions that are not present in MySQL 8.0/Heatwave, for example, JSON_DETAILED, which is called JSON_PRETTY in MySQL 8.0/Heatwave. This is not a blocking factor for migration unless these functions are present in the default values of columns. Additionally, if your application uses some of these functions, it may be necessary to modify it to use the appropriate one in MySQL 8.0/Heatwave. MariaDB Functions Documentation Data Types Data types are always a concern and need to be reviewed. MariaDB supports INET6 as a data type not present in MySQL 8.0/Heatwave. INET6 is represented as a VARBINARY(6) in MySQL 8.0/Heatwave. Another example of this would be JSON columns. In MariaDB, the JSON column is represented as a LONGTEXT data type with JSON_VALID() function checking. In MySQL 8.0/Heatwave, JSON is a native datatype allowing multiple functions and enhancements related to performance and replication. Unknown datatypes will cause the logical dump to fail, slowing down the migration. Conclusion If you are not using specific features of MariaDB, migrating from MariaDB to MySQL 8.0/Heatwave is easy. By using a logical or continuous operations approach, the migration can be done cleanly and ensure minimal interruption to daily operations. Don’t forget the more you delay the migration process, the more complicated it will become and the harder it will be to move off MariaDB. Support for migrations and beyond RheoData is the only Global Systems Integrator (SI) that can easily help you migrate from MariaDB to MySQL 8.0/Heatwave! We specialize in helping customers architect and integrate their data from one platform to the next using existing data integration tools and techniques. Let us show you how to quickly move from MariaDB to MySQL and maintain operational readiness! Contact us today: 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" : "MariaDB to MySQL 8.0/Heatwave Migrations", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/mariadb-2-mysql8-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/migrate-mariadb-to-mysql", "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" : "Occasionally, I will write a script that customers can use. When I do this, they often ask for documentation to accompany the script, which leads to more time spent than intended. At the same time, documentation is always a good thing to do in both your code and as a formal document for the end user. To that end, I have always sought a quick way to generate script documentation. Yeah, all kinds of documentation generators use comments and tags to produce documentation, but I was looking for something else. Earlier in the year, I started to play with Chat-GPT. Then, I went to Oracle Cloud World and came across Cohere. I finally found a way between both AI platforms, although not 100% in documentation style, to write documentation by simply prompting the code to an AI. Suppose you didn’t catch it at Oracle Cloud World. In that case, Oracle is partnering with Cohere and embedding “generative AI” into many of its applications, hence why I have liked Cohere over OpenAI. How does Cohere help me with my documentation? If you spend time working or playing with Cohere’s Playground, you can quickly see how the AI responds to simple requests to produce or generate text-based content. At the same time, Cohere provides you with code samples on how to run their Generative AI tool from various languages. In my case, I wanted to see how it looked with Python, and the code looked similar to this: Note: you will need to get an Trail API key from Cohere import cohere co = cohere.Client(‘API Key') # This is your trial API key response = co.generate( model='command', prompt= max_tokens=3654, temperature=0.9, k=0, stop_sequences=[], return_likelihoods='NONE') print('Prediction: {}'.format(response.generations[0].text)) As you can tell, it is straightforward code to interact with the Cohere Generative AI. How did I use it to write documentation? Using the same code above, I had to figure out how to pass a file to the AI or at least the contents of the file. As I gave this some thought, all I had to do was use two files – one as the read file and one as the write file. The read file is the Python code I wrote, and the write file is the Microsoft Word document I wanted. This resulted in a Python file that looks like this: import cohere co = cohere.Client(‘API Key') # This is your trial API key with open(“check_servicemanager.py”, “r”) as code_file: data = code_file.read() doc_file = open(“/docfile.doc”, “w”) response = co.generate( model=’command’, prompt=data, max_tokens=2500, temperature=0.5, k=0, stop_sequences=[], return_likelihoods=’NONE’) doc_file.write(response.generations[0].text) #print(‘Prediction: {}’.format(response.generations[0].text)) doc_file.close() code_file.close() A couple of things to notice: 1. I opened a file called “check_servicemanager.py” as read-only. (this is the Python code I wanted to document) 2. The data variable is set to the contents of “check_servicemanager.py” in string format 3. The file used for writing is a “.doc” file. This allows for the AI to write to a Microsoft Word document. The downside here is that when opening this file when done, there may be some compatibility issues, especially on Mac OS. The AI will write it out as a text file usually. 4. Within the co.generate block, the “prompt” is how the AI knows what to do. In this case, I set this to the variable data. Prompting the AI to use “check_servicemanager.py” as input. 5. Lastly, the code will write the response from the AI to the desired file as standard text. What was the output? You may be wondering what output was generated into the write file. This was simply an evaluation of the code as provided below: The code you provided is a Python script that checks the deployment status of a GoldenGate ServiceManager. It does this by requesting the GoldenGate server’s API and retrieving the status of the ServiceManager deployment. The script then prints the status to the console. Here is an explanation of the code: – The code starts with importing the required packages: `requests`, `json`, `os`, `sys`, and `time`. It then disables warnings and defines a function to handle control-c interruptions. – The `deployment_status` function makes a request to the GoldenGate server’s API to retrieve the status of the ServiceManager deployment. It then prints the status to the console. – The `main` routine sets the variables for the GoldenGate server’s URL, administrator username, and password. It then sets a signal handler for control-c interruptions and calls the `deployment_status` function with the ServiceManager deployment name. – The `sig_handler` function is a signal handler that is defined to handle control-c interruptions. It prompts the user to confirm if they want to exit and exits the program if the user confirms. The code appears to work as expected, but it is difficult to determine the specific behavior without the deployment name and GoldenGate server URL. It is also important to ensure that the GoldenGate server’s API is accessible and that the administrator username and password are correct. At the same time, the generated output doesn’t quite fit what you wanted for documentation; I could have run the code multiple times to get different results. I found that changing the “temperature” caused different results based on the randomness desired by changing this setting. Conclusion Using AI greatly helped me get a start on writing documentation for scripts and other items. At the same time, I still needed to adjust the output based on what I wanted documentation to say. Using AI to speed up documentation writing is good and flexible, yet it still requires a human to put some brain power into their documentation.", "author" : { "@type" : "Person", "name" : "rheostage5132", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/rheostage5132" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Writing Python documentation with Cohere’s Generative AI", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/python_doc_with_cohere-2.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/python-script-doc-cohere", "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": "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" : "If you have been following the last few blog posts, this is the final one with regards to setting up similarity search with Oracle’s upcoming vector datatype. This unique datatype enables you to be similarity search within your applications quickly and easily while keeping everything secure behind Oracle standard security of the Oracle database. If you have not keep up with the last few posts, you can go back and review the other three parts. These posts were designed to provide you with the basics of using Oracle’s Vector datatype, updating existing tables, and using similarity search from the command line. Part 1: https://rheodata.com/vector-datatype/ Part 2: https://rheodata.com/oracle-vector-datatype-updating-table-data/ Part 3:https://rheodata.com/similarity-search-oracle-vector-datatype/ In this post, we are going to look at extending Part 3 by creating a simple Flask Application to do the similarity search through a web page. To do this, there had to be a few minor changes to the previously illustrated Python code. Let’s dive in and see how a similarity search can be done via a web page. Prerequisites: Like Part 3, the prerequisites are with additional added for Flask: Oracle Database 23.4 (limited availability) Python 3.11 or later python_oracledb (2.0.0 or later (limited availability) LLM API Key (Cohere) Flask 3.0.2 Werkzeug 3.0.1 With the prerequisites set, we can now start looking at the code that will define the following application (see image below). Python/HTML This time around we are going to look at two different files – HTML and Python. This is what makes up the Flask Application we are using for this simple similarity search. The underlying table being used is the same as Part 3 – vector.video_games_vec. Before we jump into the Python code, we need to define a template for the HTML page (index.html). This is the main page of the application.                   Video Game Search:                                               Besides the CSS information, the key items to review ar the items in curly brackets (). This is how Flask setups and uses items returned from the Python code. If you would like more on Flask and how it works with HTML – check out this page: https://flask.palletsprojects.com/en/3.0.x/ Now for Python … Finally! The python code in this example is similar to the one in Part 3; however, it has been broken down into a few more functions to make it easier to use with Flask. The first thing that needs to be done is import all the required packages for the application to work: import oracledb import cohere import array import time import secrets from flask import Flask, render_template, request, redirect, url_for from flask_wtf import FlaskForm from wtforms import StringField, SubmitField from wtforms.validators import DataRequired What you will notice here is the import of Flask and Werkzeug related items. There a good bit of items that needed to be imported but makes the application easier to develop. To keep items simple, we defined the database connection into its own function. This allows us to call for the connection and get the connection in return. This is a simplified function, but keep in mind that this will only work against an Oracle Database 23c (23.4 – Limited Availability). #define database connection function def database_connection():   try:       connection = oracledb.connect(           user=vector”,           password=“”,           dsn=“xxx.xxx.xxx.xxx:1521/freepdb1”           )       print('connected’)       return connection   except:         print('Could not make a connection’) Next, we are going to define how to vectorize the video game title we are going to search for. Breaking this out into separate function allows us to call and return the vector value at anytime within the application. #define LLM embedding model def cohere_vectorize(vInput):   co = cohere.Client(0Yyj8ORoDk6MYjSb8”)   data = vInput   response = co.embed(     texts=[data],       model='embed-english-light-v3.0’,       input_type=“search_query       )   vector_value = response.embeddings[0]     return vector_value The variable “vInput” is the title that we want to vectorize for our search. The last function that we are going to define is a function to vectorize the video game title then turns it into a FLOAT64 vector that can be used with SQL for searching the database. Then return the vector string. def exec_vec(text):       vec = cohere_vectorize(text)       vec2 = array.array(d, vec)         return vec2 Application With the functions we needed defined, we can now setup the application to perform the search of the vector.video_games_vec table. First thing we need to do, is define the application. This done with the following statements: app = Flask(__name__) app.secret_key = secrets.token_hex(16) Then we need to define a class for the form itself: class Form(FlaskForm):   text = StringField('Video Game Search: ', validators=[DataRequired()])     submit = SubmitField('Submit’) Next, tell the application how to route to the page and what cURL functions to use: @app.route('/', methods=('GET', 'POST’)) Lastly, we need to define a function for the index.html page. This is simply called index(). This function sets up the following: calls the form class variables/lists needed SQL statement to use opens the database connection validate the form retrieve the required information closes the database connection The index() function looks as follows: def index():   form = Form()   output_titles = []   output_ids = []   output_genres = []   output_console = []     binds = []   select_stmt = select id, title, genres, console                   from vector.video_games_vec                   order by vector_distance(vg_vec1, :1, DOT), id                   fetch first 5 rows only”     connection = database_connection()   if form.validate_on_submit():       title = exec_vec(form.text.data)       with connection.cursor() as cursor:           for (id, title, genere, console,) in cursor.execute(select_stmt, [title]):               output_ids.append(id)               output_titles.append(title)               output_genres.append(genere)               output_console.append(console)               #print(output_titles)               #put all columns in a single list       binds = list(zip(output_ids, output_titles, output_genres, output_console))         #print(binds)       return render_template('index.html', form=form, output=binds)     connection.close()     return render_template('index.html', form=form, output=None) if __name__ == __main__”:     app.run(debug=True) A couple of key items to point out in this function. The first is the SQL statement. The select statement defines what we are looking for in the vector.video_games_vec table. In this case we are looking for ID, TITLE, GENRES, and CONSOLE. Then we are looking and ordering by the distance between each title by using the VECTOR_DATABASE function using DOT notation, then ordering by ID. Lastly, we are only fetching the first five rows only. When this application executes this SQL statement against the vector.video_games_vec table, we will be using TITLE to find the video game in the table. All rows that are returned are then broken into four different lists. Then the lists are zipped together to give us all the information for the record via the binds list. Lastly, we are telling the application to return the binds list to the output area on the index.html page before closing the connect to the database. You can use this link to see similarity search in action: Video Game Similarity Search Other items to understand You may have noticed that some video games returned more titles than expected. This is because of: We are limiting the result set to the first five rows. This is normal behavior when the result set doesn’t have exactly five of the title. The proximity of the additional titles compared to the title being searched for", "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" : "Similarity Search with Oracle’s Vector Datatype, Python, and Flask", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/similariity-search-flask-1-1024x474-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/similarity-search-python-flask", "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" : "In my last blog post on Oracle’s Vector data type, I simply showed you how the datatype is used within an Oracle table. In this blog post, we are going to dive a bit deeper and provide some context with it regardiing to updating a table with existing data. To start, we are going to look at an external table that provides data on video games. This external table is only going to be used to pull in the data we want to us. The outline of the external table is: drop table vector.video_games; create table vector.video_games ( Title VARCHAR2(50), Features.Handheld? VARCHAR2(50), Features.Max Players NUMBER, Features.Multiplatform? VARCHAR2(50), Features.Online? VARCHAR2(15), Metadata.Genres VARCHAR2(50), Metadata.Licensed? VARCHAR2(15), Metadata.Publishers VARCHAR2(50), Metadata.Sequel? VARCHAR2(15), Metrics.Review Score NUMBER, Metrics.Sales NUMBER, Metrics.Used Price NUMBER, Release.Console VARCHAR2(50), Release.Rating VARCHAR2(5), Release.Re-release? VARCHAR2(15), Release.Year NUMBER, Length.All PlayStyles.Average NUMBER, Length.All PlayStyles.Leisure NUMBER, Length.All PlayStyles.Median NUMBER, Length.All PlayStyles.Polled NUMBER, Length.All PlayStyles.Rushed NUMBER, Length.Completionists.Average NUMBER, Length.Completionists.Leisure NUMBER, Length.Completionists.Median NUMBER, Length.Completionists.Polled NUMBER, Length.Completionists.Rushed NUMBER, Length.Main + Extras.Average NUMBER, Length.Main + Extras.Leisure NUMBER, Length.Main + Extras.Median NUMBER, Length.Main + Extras.Polled NUMBER, Length.Main + Extras.Rushed NUMBER, Length.Main Story.Average NUMBER, Length.Main Story.Leisure NUMBER, Length.Main Story.Median NUMBER, Length.Main Story.Polled NUMBER, Length.Main Story.Rushed” NUMBER ) ORGANIZATION EXTERNAL ( default directory dir_temp ACCESS PARAMETERS (   RECORDS DELIMITED BY NEWLINE   FIELDS TERMINATED BY ‘,'   OPTIONALLY ENCLOSED BY ‘'   ) LOCATION ('video_games.csv’) ) reject limit unlimited; As you can see, there are a lot data points that we can use. To make this a bit simpler, we are only going to use the first 16 columns. This means we need to create a standard heap table that reference these columns. create table vector.video_games_vec (   title VARCHAR2(50),   handheld VARCHAR2(50),   maxplayers NUMBER,   multiplatform VARCHAR2(50),   availiableonline VARCHAR2(15),   genres VARCHAR2(50),   license VARCHAR2(15),   publishers VARCHAR2(50),   sequel VARCHAR2(15),   reviewscore NUMBER,   usedprice NUMBER,   sales NUMBER,   console VARCHAR2(50),   rating VARCHAR2(5),   rerelease VARCHAR2(15),   rereleaseyear NUMBER ); Notice the difference in table names. The standard heap table has an ending of “vec” compared to the external table. This is to keep our processes separate. At the same time, after we insert data into the heap table, we are only going to use the heap table. Insert data into heap table (vector.video_games_vec) based on the data in the external table (vector.video_games). insert into vector.video_games_vec; select Title”, Features.Handheld?”, Features.Max Players”, Features.Multiplatform?”, Features.Online?”, Metadata.Genres”, Metadata.Licensed?”, Metadata.Publishers, Metadata.Sequel?”, Metrics.Review Score”, Metrics.Sales”, Metrics.Used Price”, Release.Console”, Release.Rating”, Release.Re-release?”, “Release.Year from vector.video_games; In table vector.video_games_vec, we should now have a bit more than 1200 records. select count(*) from vector.video_games_vec; Returns 1209 Now we have a data set to work with. We are going to leave the external table (vector.video_games) in place for additional tests later. Add a vector column In order to use the vector.video_games table for semantic searches, we need to add a column for a vector. Since we do not know the number dimensions for the vectors or the formatting, lets assume that all data will be of any format with an unlimited dimensions. Our alter table command then looks like this: SQL> alter table vector.video_games_vec add (vg_vec VECTOR(*,*)); If we do a describe on the table, we will see the vector: Name Null? Type ---------------- ----- ------------ ID NOT NULL NUMBER(38) TITLE VARCHAR2(50) HANDHELD VARCHAR2(50) MAXPLAYERS NUMBER MULTIPLATFORM VARCHAR2(50) AVAILIABLEONLINE VARCHAR2(15) GENRES VARCHAR2(50) LICENSE VARCHAR2(15) PUBLISHERS VARCHAR2(50) SEQUEL VARCHAR2(15) REVIEWSCORE NUMBER USEDPRICE NUMBER SALES NUMBER CONSOLE VARCHAR2(50) RATING VARCHAR2(5) RERELEASE VARCHAR2(15) RERELEASEYEAR NUMBER VG_VEC VECTOR However, it doesn’t tell us size of the vector. This is limitation in the VSCode interface we are using. If we go to a command prompt, we can run the same commands and see the size of the vector. SQL> desc vector.video_games_vec; Name Null? Type ------------------------------ -------- —————————————— ID NOT NULL NUMBER(38) TITLE VARCHAR2(50) HANDHELD VARCHAR2(50) MAXPLAYERS NUMBER MULTIPLATFORM VARCHAR2(50) AVAILIABLEONLINE VARCHAR2(15) GENRES VARCHAR2(50) LICENSE VARCHAR2(15) PUBLISHERS VARCHAR2(50) SEQUEL VARCHAR2(15) REVIEWSCORE NUMBER USEDPRICE NUMBER SALES NUMBER CONSOLE VARCHAR2(50) RATING VARCHAR2(5) RERELEASE VARCHAR2(15) RERELEASEYEAR NUMBER VG_VEC VECTOR(*,*) When we query the vector.video_games_vec and look for the vector, we will see that no vector information is available. SQL> set linesize 150; SQL> select title, vg_vec from vector.video_games_vec where rownum <=5;        ID TITLE VG_VEC ---------- -------------------------------------------------- ———————————————————————————————————————— 133 Battles of Prince of Persia 134 GripShift 135 Marvel Nemesis: Rise of the Imperfects 136 Scooby-Doo! Unmasked 137 Viewtiful Joe: Double Trouble! At this point, we need a way to update the column with vector embeddings. One approach is that we can create our own vectors, but we will not be doing that in this post. Instead, we are going to use Python and make a call to a Large Language Model (LLM) like Cohere or ChatGPT to get our embeddings. With deciding on using a LLM to embed our table data, the following questions need to be asked: Do we embed the whole row? Do we embed individual columns? For this post, we are going to embed a single column. This column we are going to use is “Title”. To update the vector column for all rows within the table, we need to ensure that a primary key is defined. In our case, the primary key is “ID”. Python To update all the records in the table, we need to loop through all the records and update the record based on the primary key. In this case, the primary key is “ID”. First, we need to import the required Python packages: #Setup imports required import os import sys import array import time import oracledb import cohere Then we need to setup our API key for Cohere. Keep in mind that the testing API key for Cohere can only do ten calls per minute. If you need to do large tables, hundreds plus records, you may need to get a production key. #set Cohere API key api_key = “triZDP9cGrfwtxwb99IgM3hrt3txs co = cohere.Client(api_key) With imports and api key set, we now need to setup a database connection. With python there are multiple ways of making a connection; in this case we are going to define database connection function that can be used later. #define database connection function def database_connection():   try:       connection = oracledb.connect(           user=”,           password=”,           dsn=xxx.xxx.xxx.xxx:1521/”           )       print('connected’)       return connection     except: print('Could not make a connection’) Next, we are defining the SQL statements that are going to be ran to identify the records we want, how to update the vector column, and then select the updated records to confirm that they were updated. These are set as variables within the script as follows: fetch_query = select id, title from vector.video_games_vec where id between 71 and 75 order by id” select_query = select id, title, vg_vec1 from vector.video_games_vec where id between 71 and 75 order by id” sql_update = update vector.video_games_vec set vg_vec1 = :1 where id = :2” Notice that we are using a simple “between” statement with the SQL statements to limit the number of rows. This is only for testing purposes and not to make the script automated. Next, we are going to connect to the database based on the previously defined function. connection = database_connection() Everything we need to update our table is now in place. Using the connection, we are going to setup another cursor for querying the data and then looping through it and update the required rows. with connection.cursor() as query_cursor:   #prepare the select statement     query_cursor.prepare(fetch_query)   #define arrays being used   ids = []   data = []   vec = []   vrows = []     rows_returned = 0   #execute the select statement     query_cursor.execute(fetch_query)   #get all the rows/data returned     rows = query_cursor.fetchall()   #get the number of rows returned     rows_returned = query_cursor.rowcount   print('Got ' + str(rows_returned) + ' rows’)     #print(rows[0])     #Process row into list sets   for row in rows:       ids.append(row[0])       dat = 'query: ' + row[1]         data.append(dat)   #Get length of lists for the ids (in this case 10)     id_len = len(ids)   #Vectorize the data within one interation     for x in range(0, 1):       response = co.embed(           texts=data,           model='embed-english-light-v3.0’,           input_type=“search_query             )         #format and remember vectors for all records returned       for y in range(0, id_len):           vec = response.embeddings[y]           #Set vector to FLOAT32             #vec2 = array.array(f, vec)           #Set vector to FLOAT64           vec2 = array.array(d, vec)           #append add the ids and embeddings to an array             vrows.append([ids[y], vec2])             print(Tuple -> + str(ids[y]) + ', '+ str(vec))             #Update tuple in table           try:               update_cursor = connection.cursor()               update_cursor.setinputsizes(None, oracledb.DB_TYPE_VECTOR)               update_cursor.execute(sql_update, [vec2, ids[y]])               connection.commit()           except:                 print(Unable to update table\n”) #Select the records that have been updated. try:   select_cursor = connection.cursor()   select_cursor.prepare(select_query)     select_cursor.execute(select_query)   for row in select_cursor:         print(row) except:     print(Cannot select from table”) Once we run this python code, we now have records in the database updated with vectors that are related to the title of the video game. In the next blog post, we will take a look at how to do a semantic search using python and the Oracle Vector Datatype.", "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 Vector Datatype – Updating table data", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/Oracle-Vector-Datatype-Updating-1-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-vector-datatype-updating-table-data", "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" : "HashiCorp is constantly improving their HashiCorp Vault Open Source System (OSS) product and maturing it into HashiCorp Vault Enterprise. With this maturity, there is often the questions of what is the right approach for the organization or is now the time to make the switch to an enterprise release of the product? Yes, HashiCorp Vault OSS is very powerful and secure secrets management system; however, when should your organization bring it out of the shadows and into the light by maturing to the HashiCorp Vault Enterprise platform? After all, it shouldn’t be running under a developers desk! RheoData makes it easy for your organization to identify what you are currently running as well as how your HashiCorp Vault implementation could scale. By using our free HashiCorp Vault Health Check, an organization can get a sense of what their Hashicorp Vault deployment is doing and how it is being used. Contact us today to get access to RheoData’s HashiCorp Vault Health Check tool — here (go to bottom of page to download). This is a free tool to download and RheoData is looking forward to helping you with your HashiCorp Vault needs. Download and Enjoy!!!", "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" : "HashiCorp Vault Health Check … Is your HashiCorp Vault healthy?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/vault_hc-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/hashicorp-vault-health-check-get-a-checkup-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" : "“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" } } ```