--- title: RheoData Blog | Cloud (3) description: Cloud | RheoData Blog Posts (3) --- [![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 (3) ## [Identifying Predefined Administrative Accounts in Autonomous Database or any other Oracle database](https://rheodata.com/en-us/blog/identifying-predefined-administrative-accounts-in-autonomous-database-or-any-other-oracle-database) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:08 PM A quick post as a reminder for myself some time down the road. After all, we all need reminders... [CONTINUE READING](https://rheodata.com/en-us/blog/identifying-predefined-administrative-accounts-in-autonomous-database-or-any-other-oracle-database) ## [Installing Nginx on AWS EC2 instance](https://rheodata.com/en-us/blog/installing-nginx-on-aws-ec2-instance) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:05 PM I’ve been helping clients with Oracle GoldenGate (Microservices) implementation – one thing that... [CONTINUE READING](https://rheodata.com/en-us/blog/installing-nginx-on-aws-ec2-instance) ## [Transforming Oracle GoldenGate Operations with AI: Building MCP Servers for Real-World Impact](https://rheodata.com/en-us/blog/transforming-oracle-goldengate-operations-with-ai-mcp-servers) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:04 PM Remember when everyone said the cloud was just a fad? We’re hearing similar skepticism about AI in... [CONTINUE READING](https://rheodata.com/en-us/blog/transforming-oracle-goldengate-operations-with-ai-mcp-servers) ## [Oracle’s Vector Datatype](https://rheodata.com/en-us/blog/vector-datatype) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:03 PM At Oracle Cloud World 2023, Oracle announced they were moving toward enabling Artificial... [CONTINUE READING](https://rheodata.com/en-us/blog/vector-datatype) ## [Configuring Nginx on AWS EC2 for Oracle GoldenGate 21c](https://rheodata.com/en-us/blog/configuring-nginx-on-aws-ec2-for-oracle-goldengate-21c) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:01 PM When installed, Oracle GoldenGat (Microservices) will set up “services” that require a port number... [CONTINUE READING](https://rheodata.com/en-us/blog/configuring-nginx-on-aws-ec2-for-oracle-goldengate-21c) ## [Why hire a RheoData Consultant](https://rheodata.com/en-us/blog/why-hire-a-rheodata-consultant) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:01 PM [CONTINUE READING](https://rheodata.com/en-us/blog/why-hire-a-rheodata-consultant) ## [Building Oracle GoldenGate Cloud Service (GGS) with Terraform](https://rheodata.com/en-us/blog/building-oracle-goldengate-cloud-service-ggs-with-terraform) Posted by [Fame](https://rheodata.com/en-us/blog/author/fame) | Nov 10, 2025 9:30:00 PM A couple of weeks ago Oracle released Oracle GoldenGate Service (aka. Oracle GoldenGate Cloud... [CONTINUE READING](https://rheodata.com/en-us/blog/building-oracle-goldengate-cloud-service-ggs-with-terraform) ## [Initial Load from Tandem (HP-UX) to AWS Kafka](https://rheodata.com/en-us/blog/initial-load-from-tandem-hp-ux-to-aws-kafka) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:59 PM Working with a customer where we needed to move data from a Tandem (HP-UX Guardian) system up to an... [CONTINUE READING](https://rheodata.com/en-us/blog/initial-load-from-tandem-hp-ux-to-aws-kafka) ## [Oracle to Azure Migration: Oracle@Azure vs. SQL Server – The Strategic Choice That Drives Results](https://rheodata.com/en-us/blog/oracle-azure-vs-sql-server-migration) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:58 PM [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-azure-vs-sql-server-migration) ## [The transition has begun … Oracle GoldenGate merging into Oracle Database 21 (new features)](https://rheodata.com/en-us/blog/the-transition-has-begun-oracle-goldengate-merging-into-oracle-database-21-new-features) Posted by [rheostage5132](https://rheodata.com/en-us/blog/author/rheostage5132) | Nov 10, 2025 9:29:58 PM News on the Oracle GoldenGate front!!!!! 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[vector database consolidation (1)](https://rheodata.com/en-us/blog/tag/vector-database-consolidation) See all - - [1](https://rheodata.com/en-us/blog/tag/cloud) - [2](https://rheodata.com/en-us/blog/tag/cloud/page/2) - [3](https://rheodata.com/en-us/blog/tag/cloud/page/3) - [4](https://rheodata.com/en-us/blog/tag/cloud/page/4) - [5](https://rheodata.com/en-us/blog/tag/cloud/page/5) - ##### About RheoData RheoData is based out of Metro Atlanta, GA and provide expert Oracle, Microsoft, Google, and Snowflake services.  Let us  know how we can help! ##### Links - [About Us](https://rheodata.com/who-we-are) - [FrostCore](https://rheodata.com/frostcore) - [RedCore](https://rheodata.com/redcore) - [BlueCore](https://rheodata.com/bluecore) ##### Contact us [hello@rheodata.com](mailto:hello@rheodata.com) ©RheoData2026. 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 (3)", "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/3", "@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" : "A quick post as a reminder for myself some time down the road. After all, we all need reminders every once in a while! Prior to Oracle Database 12c finding a list of default users within the database was always a pain. In Oracle Database 12c or later, this has become much simpler. Oracle introduced a column on the *_users views that allows the end users to identify what users are maintained by Oracle. This column name is ORACLE_MAINTAINED. The value of the column is either “N” or “Y”. If it is “Y”, then user account is maintained by Oracle. A simple query of the ALL_USERS view will return about 57 different accounts that are maintained by Oracle in Oracle Database 19c (Autonomous Data Warehouse). select * from all_users where oracle_maintained = 'Y' order by username; For more information, the Oracle Database 19c docs can be referenced here.", "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" : "Identifying Predefined Administrative Accounts in Autonomous Database or any other Oracle database", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/identify_predefined_accounts-1024x638-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/identifying-predefined-administrative-accounts-in-autonomous-database-or-any-other-oracle-database", "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" : "I’ve been helping clients with Oracle GoldenGate (Microservices) implementation – one thing that keeps coming up is how Nginx is installed. Each environment has different approaches. I wrote how to install this on Oracle Enterprise Linux in an earlier approach. Now, I have to provide a way to install Nginx on the AWS EC2 instance to support Oracle GoldenGate (Microservices). Before installing NGINX, ensure that you have an active EC2 compute node on AWS. In my case, I’m using an EC2 instance on the AWS Free Tier. Now that I have an EC2 compute node, the first thing to do is SSH into it. $ ssh -I ~/.ssh/rd-usf-tst.pem ec2-user@ Note: The pem file permissions should be 400 (chmod 400). Then ssh’ing into the EC2 node will go straight through. With being logged into the ECS instance, you’ll need to su over to the root user. $ sudo su -  After logging in as the root user, you’ll need to run “amazon-linux-extras” to get nginx installed. $ amazon-linux-extras install nginx1.12 What is interesting after confirming installation, the AWS output says that the “extra” items has reached the end of support. From what I can tell this really doesn’t mean anything other than AWS needs to update it soon. Just be aware. To confirm that NGINX has been installed, use the “list” sub-command for “yum”. $ yum list install nginx Now with NGINX installed, you can configure Oracle GoldenGate (Microservices) to use it. 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" : "Installing Nginx on AWS EC2 instance", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/aws-extras-1-3.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/installing-nginx-on-aws-ec2-instance", "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" : "Remember when everyone said the cloud was just a fad? We’re hearing similar skepticism about AI in enterprise data management today. But here’s what I’ve learned after thirty years in technology leadership: the organizations that dismiss transformative technologies early often spend the most catching up later. The difference this time? AI isn’t just changing how we work with data—it’s fundamentally reshaping what’s possible when humans and systems collaborate intelligently. I’ve watched teams struggle with Oracle GoldenGate troubleshooting for hours, digging through logs and configuration files, when the right AI integration could surface those insights in minutes. That breakthrough moment came when we discovered Anthropic’s Model Context Protocol. Suddenly, we weren’t just talking about AI as some distant future capability—we had a practical, secure way to connect AI directly to the enterprise systems our teams use every day. The Challenge: When AI Meets Enterprise Reality Walk into any enterprise data center, and you’ll find the same frustrating disconnect. Marketing teams are buzzing about AI transformation while database administrators are still manually checking replication lag at 2 AM. The promise is compelling, but the execution? That’s where most organizations stumble. I’ve seen too many AI projects that look impressive in demos but crumble under real-world operational pressure. Teams invest months building fragile custom integrations that break every time Oracle releases an update. Meanwhile, the DBAs who could benefit most from AI assistance continue working with the same tools they’ve used for years, waiting for someone to bridge that gap between possibility and practicality. Oracle’s microservices framework already gives us solid programmatic access through REST APIs. We can automate deployments and monitor replication status dynamically. But imagine if your database administrators could simply ask, “Which extracts are running slowly today?” and get immediate, accurate answers from live system data. The Model Context Protocol: Finally, a Standard That Makes Sense At Google NEXT this year, when Anthropic announced the Model Context Protocol, I knew we’d found something different. This wasn’t another flashy AI demo—it was a practical solution to the standardization problem that’s been holding back enterprise AI adoption. Think of MCP as creating a universal connection standard between AI and your existing infrastructure—like how USB-C eliminated the chaos of proprietary cables. One standardized interface that works across different AI platforms and evolves with your systems. But here’s what makes MCP genuinely powerful for enterprise environments: it respects your security boundaries. Instead of training AI models on sensitive data, MCP lets AI access live information through controlled, secure interfaces. Your critical data stays exactly where it belongs while AI gains the context it needs to provide meaningful assistance. Building MCP Servers That Actually Work in Production Let me show you how we approach MCP development for Oracle GoldenGate, using principles that work for any enterprise system integration. The architecture starts simple but reflects years of hard-won lessons about enterprise integrations: # server.py - Your integration foundation import sys from mcp.server.fastmcp import FastMCP mcp = FastMCP(GoldenGateMCP) # main.py - Your execution entry point from server import mcp if __name__ == __main__: mcp.run(transport='stdio') This foundation looks straightforward, but what’s happening underneath reflects everything we’ve learned about building systems that teams can depend on. Clean separation between configuration and execution makes managing different environments infinitely easier. Tools That Solve Real Problems The real work happens when you build tools around business operations, not just technical functions. Here’s how we structure our Oracle GoldenGate integration: class GoldenGateOperations: def __init__(self, config: dict = None): if config is None: from config_loader import load_config config = load_config() # Secure configuration management gg_config = config[golden_gate] self.host = gg_config[host] self.username = gg_config[username] self.process_configs = gg_config[processes] async def get_process_status(self, process_type: str): Retrieve real-time status for GoldenGate processes if process_type not in self.process_configs: available_types = list(self.process_configs.keys()) raise ValueError(fProcess type '{process_type}' not configured. Available: {available_types}) # Secure API interaction with proper authentication config = self.process_configs[process_type] url = fhttp://{self.host}:{config['port']}/services/v2/{config['endpoint']} # Enterprise security practices auth_credentials = f{self.username}:{config['password']} encoded_auth = base64.b64encode(auth_credentials.encode()).decode() headers = {Authorization: fBasic {encoded_auth}} response = requests.get(url, headers=headers, verify=False) return json.dumps(response.json(), indent=2) See what we’re doing here? Configuration stays external, authentication follows security best practices, and error handling gives clear feedback. These aren’t just coding preferences—they’re operational necessities when building systems that teams will stake their reputation on. Natural Language for Operations Teams The transformation happens when you expose these capabilities through conversational interfaces: @mcp.tool() async def get_extract_status(): Check the current status of all extraction processes return await gg_operations.get_process_status(extracts) @mcp.tool() async def get_replicat_status(): Monitor replication process health and performance return await gg_operations.get_process_status(replicats) Suddenly, your operations team can ask “Are all our extracts running normally?” and get immediate answers based on live system data. That’s when AI stops feeling like a science project and starts delivering real value. Strategy Beyond the Code Building the MCP server is the foundation, but successful implementation requires thinking strategically about how teams actually work. Enhancing Existing Workflows The most successful AI implementations enhance established processes rather than disrupting them. Your database administrators already have proven workflows for monitoring GoldenGate environments. Smart MCP servers accelerate these processes without forcing teams to abandon what’s already working. Starting with monitoring and status checking creates immediate value while building confidence. Teams can verify AI responses against familiar tools, gradually building trust in the system. Security That Actually Works Enterprise AI demands enterprise-grade security from day one: Credential Management: Enterprise-grade secrets management, never hardcoded passwords Network Security: Proper network segmentation and access controls Audit Logging: Complete tracking of all AI interactions with enterprise systems Data Governance: AI interactions that comply with organizational data policies Getting Teams on Board Technical implementation is often the straightforward part. Real success requires thoughtful change management: Start with Power Users: Find team members comfortable with new technology who can become advocates Demonstrate Clear Value: Show concrete time savings and improved accuracy, not just impressive technology Provide Fallback Options: Teams need confidence they can perform critical tasks even if AI systems are unavailable Iterate Based on Feedback: The best implementations evolve based on real user experiences Connecting to Production Systems Once your MCP server is tested and ready, connecting it to platforms like Claude Desktop requires attention to operational details. Configuration management becomes critical here. Your claude_desktop_config.json should reflect organizational deployment standards: { mcpServers: { GoldenGateOperations: { command: /opt/python/bin/uv, args: [ --directory, /opt/mcp/goldengate-server, run, main.py ] } } } Use absolute paths, establish consistent deployment locations, and ensure your configuration integrates with existing DevOps processes. These operational details determine whether your solution scales across the enterprise or remains a clever proof of concept. What Success Looks Like in the Real World When MCP servers work effectively, they transform how teams interact with complex enterprise systems. Here’s what we’ve observed: Faster Problem Resolution: Database administrators diagnose replication issues in minutes instead of hours, using natural language queries to quickly identify bottlenecks and configuration problems. Better Team Collaboration: Operations teams share system status and troubleshooting insights more effectively when AI provides context-rich explanations of system behavior. Smarter Decision Making: Managers get real-time operational insights without needing deep Oracle GoldenGate expertise. Reduced Learning Curves: New team members become productive faster when they can ask systems questions in plain English. The Bigger Picture MCP servers represent more than just another integration approach. They signal a fundamental shift toward more accessible, context-aware enterprise operations. Organizations wrestling with complex data architectures and the demand for real-time operational intelligence will find standardized protocols like MCP becoming essential infrastructure. Teams that embrace these approaches early gain significant competitive advantages in operational efficiency and system reliability. The key? Approach these implementations with the same discipline and strategic thinking you’d apply to any critical enterprise system. This isn’t about experimenting with AI—it’s about building production-ready solutions that genuinely enhance your team’s capabilities and improve business outcomes. Summary Building MCP servers for Oracle GoldenGate operations demonstrates a practical approach to enterprise AI integration—one that delivers immediate value while establishing the foundation for broader transformation initiatives. The technical implementation is straightforward when you apply proper architectural thinking and enterprise-grade practices. The real value emerges when operations teams can interact with complex systems using natural language while maintaining security, reliability, and compliance. Success requires balancing technical capabilities with thoughtful change management, ensuring AI enhancements integrate seamlessly with existing workflows and team practices. Organizations taking this measured, strategic approach build AI capabilities that scale across their enterprise architecture while delivering measurable operational improvements. The question isn’t whether AI will transform enterprise data operations—it’s whether your organization will lead that transformation or spend years catching up. MCP servers provide a clear, practical path forward for teams ready to bridge the gap between AI potential and enterprise reality. What’s our objective here? Clear communication, team success, and technology that serves the mission. That’s strategic AI integration in practice. Ready to Transform Your Enterprise Operations? At RheoData, we’ve built MCP servers and AI integrations for some of the most demanding enterprise environments. We understand the difference between impressive demos and production-ready solutions that your teams can depend on. Whether you’re looking to enhance Oracle GoldenGate operations, integrate AI with other enterprise systems, or develop a comprehensive AI strategy that aligns with your business objectives, our team brings the architectural expertise and operational experience to make it happen. Let’s coordinate on your next AI integration project. Contact us to discuss how MCP servers can transform your enterprise operations while maintaining the security, reliability, and performance your business demands. Ready to get started? Reach out to cloud@rheodata.com or visit rheodata.com to learn more about our enterprise AI integration services. Your success is our success. Let’s build something remarkable together.", "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" : "Transforming Oracle GoldenGate Operations with AI: Building MCP Servers for Real-World Impact", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_589427987-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/transforming-oracle-goldengate-operations-with-ai-mcp-servers", "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" : "At Oracle Cloud World 2023, Oracle announced they were moving toward enabling Artificial Intelligence (AI) within many of their products. Oracle is making huge steps forward for many people to use AI daily. As 2023 ended, many other industry leaders announced they would do the same. Regarding databases, Oracle is the only industry leader that leverages its core product for many different things. For at least a decade, Oracle has turned the Oracle Database into a Swiss army knife by enabling it to support different modern data types, analytics, and development paradigms, all in one product. It is only natural that with the AI revolution starting, Oracle would build a data type that enables organizations to use Retrieval-Augmented Generation (RAG) within the databases. By adding a “vector” datatype, Oracle simplifies data architectures and the building of RAG or Private-LLM configurations for organizations. Where is the Vector datatype? If you use an Oracle Database today, you will not immediately have access to the Vector datatype. Even if you use the latest version, 23.3.x.x, on Oracle Cloud Infrastructure (OCI), you cannot access this datatype (Believe me, I tried). You have to be part of the beta program for the next release of Oracle Database, which will provide you details on the Vector datatype before the initial release in 23.4. In short, and for the moment, if you are not part of the beta program, this datatype will be available soon! What is the Vector datatype? The Vector datatype is a modern datatype designed to efficiently store, manage, and index massive amounts of high-dimensional data. This data type is growing in interest and is used to create additional value for generative AI use cases and applications. Vector Settings? The vector datatype is used within standard Oracle tables. This enables database schemas to use the data in real-time. The following command shows a simple example: sql> CREATE TABLE rd_vectors (id NUMBER, embed VECTOR); This simple example shows that the vector datatype can be set as a column within a table. Enabling it this way allows you to specify vectors of different dimensions with different formats. Think of this as a catch-all setting for vector data. It is great to have a catch-all; however, you can limit the type of vectors created by imposing constraints on the stored data. In this example, you can only store up to 1024 dimensions, and they must be formatted as INT8 (8-bit integers): sql> CREATE TABLE rd_vectors_int8 (id NUMBER, embed VECTOR(1024, INT8); With this complex example, you must have 1024 dimensions, each of which must be 8-bit integers (INT8). The number of dimensions should be greater than 0 with no limit. The dimensions formats are INT8, FLOAT32, and FLOAT64. FLOAT32 and FLOAT64 are the IEEE standards, and the Oracle Databases will automatically cast the values as needed. Examples of setting additional dimension formats are: sql> CREATE TABLE rd_vectors_int8 (id NUMBER, embed VECTOR(1024, FLOAT32); sql> CREATE TABLE rd_vectors_int8 (id NUMBER, embed VECTOR(1024, FLOAT64); Vector Forms? With the understanding of Vector settings, there are a few forms that a vector can take. Understanding these forms will help in defining the proper vector for your requirements: Important Note: A vector can be NULL, but the dimensions cannot be NULL. (example: You cannot have [(1.1, NULL, 2.3)] Examples of Vectors: Now that you understand the Vector datatype, how does the Oracle Database see the datatype? The following SQL example shows that the table rd_vector is created with only vector datatypes using different variations. sql> CREATE TABLE vector.rd_vector ( v1 VECTOR, v2 VECTOR(3, FLOAT32), v3 VECTOR(2, FLOAT64), v4 VECTOR(1, INT8), v5 VECTOR(1, *), v6 VECTOR(*, FLOAT32), v7 VECTOR(*, *) ); sql> desc vector.rd_vector; Name                                         Null?   Type ----------------------------------------- -------- —————————————— V1                                                         VECTOR(*, *)  V2                                                         VECTOR(3, FLOAT32) V3                                                         VECTOR(2, FLOAT64) V4                                                         VECTOR(1, INT8) V5                                                         VECTOR(1, *) V6                                                         VECTOR(*, FLOAT32) V7                                                         VECTOR(*, *) Hopefully, Oracle will release Oracle Database 23.4 soon! There will be many opportunities to use vector data types as organizations expand their usage of Generative AI. 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" : "Oracle’s Vector Datatype", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/oracle-vector-datatype-1-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/vector-datatype", "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" : "When installed, Oracle GoldenGat (Microservices) will set up “services” that require a port number for access. For the first run of the Oracle GoldenGate Configuration Assistant (OGGCA), the assistant will create the ServiceManager (1 port) and the first deployment (5 ports). That means six ports would need to be opened on a firewall to run a single Oracle GoldenGate (Microservices) deployment. This only increases by five ports for each deployment built under a single ServiceManager. To address this, an open-source solution called NGINX allows all the ports within the deployment to be consolidated to a single port. The single port that can be used with NGINX is either 80 (HTTP) or 443 (HTTPS); which will enable Oracle GoldenGate (Microservices) to be used over a standard firewall port Downloading Binaries The NGINX binaries need to be downloaded regardless of where you install Oracle GoldenGate (Microservices). For Oracle Enterprise Linux, I documented the process here. When you start to expand the installation base for Microservices, the installation of NGINX becomes similar yet different. To install NGINX on an Amazon Web Services (AWS) EC2 instance, the following steps should be followed. For more details, you can also reference this blog post on installing Nginx -> here or here. 1. SSH into the AWS EC2 instance $ ssh -I ~/.ssh/rd-usf-tst.pem ec2-user@ 2. Sudo to Root $ sudo su – 3. Use Amazon Extras to install NGINX $ amazon-linux-extras install nginx1.12 4. Confirm installation $ yum list install nginx After installing the NGINX on the AWS EC2 instance, the next thing that must be done is to configure it against the Oracle GoldenGate (Microservices) environment. Configure NGINX Oracle has made it easy for Oracle GoldenGate administrators to configure the NGINX interface after the installation. They provided a script called “ReverseProxySettings” in the $OGG_HOME/lib/utl directory. This script is used to build the Nginx configuration file based on the deployments running on the AWS EC2 node. The steps to configure the reverse proxy are as follows: 1. Change to the Reverse Proxy directory under $OGG_HOME $ cd $OGG_HOME/lib/utl/reverseproxy 2. Run ReverseProxySettings with options (unsecure access) $ ./ReverseProxySettings -u oggadmin -P -o ogg.conf http://localhost: 3. Copy the config file to the NGINX directory $ sudo cp ogg.conf /etc/nginx/conf.d/nginx.conf 4. Create a dummy cert $ sudo sh /etc/ssl/certs/make-dummy-cert /etc/nginx/ogg.pem 5. Start NGINX $ sudo nginx & 6. Test/validate NGINX config $ sudo nginx -t 7. Reload NGINX $ sudo nginx -s reload 8. Access the ServiceManager and other services without port numbers Note: The ogg.pem must be downloaded and uploaded to your local cert wallet to access via a web browser. End Result Once everything with the NGINX is configured, Oracle GoldenGate (Microservices) can be accessed by URL using the standard port of 80 (HTTP) or 443 (HTTPS). This enables Oracle GoldenGate (Microservices) environments to be accessed over standard firewall rules.", "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" : "Configuring Nginx on AWS EC2 for Oracle GoldenGate 21c", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/nginx-url-noport-1-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/configuring-nginx-on-aws-ec2-for-oracle-goldengate-21c", "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" : "Are you looking to migrate your workloads to the cloud? Is your organization on the path to cloud yet struggle to get your workloads migrated successfully? Is cloud optimization a top priority for your organization? If your answer to these three questions is “YES”, then a RheoData consultant can help you navigate the migration path way to the cloud. The journey to the cloud doesn’t have to be a struggle and it is wise to use external consultants to bring a deep specialized knowledge plus experience that can be leveraged expedite project timelines. Here are a few reasons why RheoData consultants should be used for your next project: Independence – Unlike a full-time employee, who may be subject to internal politics of an organization, consultants bring an independent perspective to their work. A good consultant will always be unbiased and objective, as they have no personal connection to the organization. Their out-of-the-box thinking means they can focus only on the goal or plan agreed with their client, without being caught up in internal distractions. Creative thinking – Without being tied to certain ways of doing things, or a company’s historic culture and methods, consultants can also deploy their objectivity toward important creative solutions. Without having to consider whether their future work might depend on currying favor with a business’ executives, they are able to deliver breakthrough insights and strategic thinking at odds with what a client might typically have thought of. Expertise – A consultant will usually operate in narrow areas, meaning they can bring detailed and in-depth expertise required for any given sector or organization. On top of the in-depth training many firms will furnish a consultant with, advisors will work multiple clients in their specialist area, making them aware of the latest trends and developments in the field – enabling organizations to stay ahead of the curve and get the maximum potential out of new methods and models. Industry best practices – The diversity in a consultant’s experience will put them in a good position to provide insight in best practices. By learning from the best performers in the industry, organizations can find ways of improving their own operations, while holistic bigger-picture thinking of strategic consultants can put this toward enlarging an organization’s market footprint, expanding its product offerings, helping reorganize for efficiency and cost savings, increase capabilities or even acquire another company Credibility – Sometimes organizations just need a trusted pair of hands to oversee an important change project. In an industry which does not require charter status, reputation often serves as quality assurance often serves as quality assurance, as consultancies will well-known track-records have demonstrated expertise in a wide variety of fields and work with thousands of clients across the globe every year to solve various business problems and drive growth. Capacity – There will be times when any firm will eye a move for which it simply does not have the relevant talent pool or expertise – but that only necessitates a short-term contracting of the required skill set. Digitalization is a strong example of this. Given the speed of digitalization and state of competitiveness across all sectors, a company might not have the time to implement new digital infrastructure by itself in time. In the case, hiring a digital or technology consultant to help need the urgent need to build capacity with speed and scale is necessary. Keeps business running – Executing a transformation project using existing staff could leave the daily operations of an organization neglected or understaffed. By sourcing external consultants to help lead transformation projects, companies ensure that their day-to-day functions are well supported by their staff, while a team helmed by contractors and supported by a feasible team of internal employees can pursue project-driven change. Difficult decisions – By virtue of being objective, consultants can also be tasked with making difficult decisions. The process of identifying redundancies and implementing staff cuts, for instance, can be influenced by and damaging for team dynamics if administered internally. Consultants, meanwhile, have an objective lean through which they can identify where to make cuts and make them with enough emotional distance. Global scale – Some of the largest consulting firms in the world have operations in more than 100 countries. Consultants from these firms have reach in the global market and have the capacity to help a business scale up at a local, regional, or global level, which most large consulting firms have vast client networks in a variety of geographies and business domains, all of whom can potentially offer support. This can be a major boost to businesses scaling up across industries, borders, or markets. Cost effective – Beyond the immediate fees paid to the consultant, organizations deploying external expertise for individual projects do not incur overhead costs, such as providing benefits, or even having to supply a computer and a workspace. In addition, once a project is completed, consultants can be retired, meaning that an organization no longer incurs costs. In comparison, when hiring a permanent member of staff, companies will need to pay their salary all year long, while if an organization hands a permanent contract to someone who turns out not to be a good fit, it can be an expensive and exhausting process to have to remove them and start again. By using a RheoData Consultant, you can be assured that you are getting the best consultant that will help your organization drive your project to completion while sharing industry best practices. Get in touch with our sales (sales@rheodata.com) today to schedule your consultant!", "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" : "Why hire a RheoData Consultant", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/consulting_img-1024x410-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/why-hire-a-rheodata-consultant", "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" : "A couple of weeks ago Oracle released Oracle GoldenGate Service (aka. Oracle GoldenGate Cloud Service)(GGS). This is the OCI version of Oracle GoldenGate and is mostly the Oracle GoldenGate Microservices platform once built. As with all OCI items, you can login to the Oracle Cloud and build the service via a click through process. In this post, I want to show you a quick way to deploying the service using HashiCorp Terraform and few items I found. Note: The items I’m pointing out, Oracle Product Management is aware of these and should be working on fixing them. After logging into Oracle Cloud (OCI), you will find GoldenGate under Database Related Services. In this menu item, there are three options. The Deployment option is what you need to be concern with. After selecting the Deployment option, you will end up at a page where you will see the deployments that have been created already. In the image below, I’ve been testing and it takes 24 hours to remove a deployment once it is deleted. Now that you know where you want to look to see the deployments after they are created, lets look at how to build an Oracle GoldenGate Service (GGS) using Terraform. The information you need to build Oracle GoldenGate Service (GGS) via Terraform is located in the Terraform Registry. This information will tell you all the required and optional items needed to build the service. There are a few things to be aware of though, the first is deployment_type. What type of deployment are you going to build? The short story is that the only deployment type is “OGG”. If you do not provide this value, then the Terraform deployment will error with the following: Error: Missing required argument on main.tf line 40, in resource “oci_golden_gate_deployment” “ggdemo”: 40: resource “oci_golden_gate_deployment” “ggdemo” { The argument “deployment_type” is required, but no definition was found. After resolving this issue, the next issue to watch out for has to do with the License. In the GUI, you can select “BYOL” or “License Included”. These two options provide you a way to either minimize the cost (BYOL) or incur more costs (License Included), depending on how you look at it. If you go the “BYOL” approach, this assumes you already have a license of Oracle GoldenGate that you are transferring to the cloud. The “License Included” approach adds a minimal amount of cost to you cloud credit rate. Use what benefits your organization. Where the problem comes in at is that with Terraform, both values are valid but only one actually works. The one that actually works for now, is “License Included”. What happens if you choose “BYOL” from the Terraform side is that the process will error out after two minutes. An error similar to this will be displayed: Error: 500-InternalError Service: GoldenGateDeployment Error Message: Internal error occurred OPC request ID: acf6c14f0506ae2d4b3ee53ac4f694f9/0A147B86DFDB4F91B6AA1E24629864C4/B449B42E1D181A4D5C5A6CFBF3F603FB Suggestion: The service for this resource encountered an error. Please contact support for help with service: GoldenGateDeployment Now that the “gotchas” are out of the way, what does a simple Terraform script look like for building Oracle GoldenGate Service (GGS)? The below script is what I use to build a quick deployment for testing: resource oci_golden_gate_deployment ggdemo { #Required compartment_id = ocid1.compartment.oc1..aaaaaaaade5bxtniugmwuiynsonpq74fo2djk6hd64qu3lzw2xybym….q cpu_core_count = 2 deployment_type = OGG display_name = GGDEMO is_auto_scaling_enabled = false is_public = true license_model = LICENSE_INCLUDED subnet_id = var.testsubnet ogg_data {           admin_password = “**************”           admin_username = “oggadmin           deployment_name = “GGDEMO      } } The above script is using only the required options for the Oracle GoldenGate Service (GGS) deployment. In this script, I’m setting the compartment where I want the deployment built at. Then I’m telling is how many OCPU it should have. Already covered the deployment type. What name I want to call the deployment and if the deployment should be auto scaling. In this case, didn’t turn on auto-scaling. Then I’m making sure I assign a public IP address and adding to an existing subnet (VCN). Lastly, I’m providing what I want for the GoldenGate Microservices login. After running a terraform plan -out plan.out and terraform apply plan.out; Terraform will begin building the GGS deployment. This can be seen in the deployment page that was mentioned earlier. After about 11-15 minutes, you will have an Oracle GoldenGate Service that you can access by a given URL. With the deployment done, I’m presented with the following output: oci_golden_gate_deployment.ggdemo: Creation complete after 13m2s [id=ocid1.goldengatedeployment.oc1.iad.amaaaaaaxbdlgqiancxr3umcvfatxlztc4llzztudhlpicbb3la57ob4zpoa] data.oci_golden_gate_deployment.ggdemo: Reading... data.oci_golden_gate_deployment.ggdemo: Read complete after 0s [id=ocid1.goldengatedeployment.oc1.iad.amaaaaaaxbdlgqiancxr3umcvfatxlztc4llzztudhlpicbb3la57ob4zpoa] Apply complete! Resources: 1 added, 0 changed, 0 destroyed. The state of your infrastructure has been saved to the path below. This state is required to modify and destroy your infrastructure, so keep it safe. To inspect the complete state use the `terraform show` command. State path: terraform.tfstate Outputs: ggdemo = “https://3la57ob4zpoa.deployment.goldengate.us-ashburn-1.oci.oraclecloud.com/” By using the URL given, I can access the Oracle GoldenGate Service and login. At this point, if everything is setup correctly, I can quickly build out the replication environment using the REST APIs. Enjoy!!", "author" : { "@type" : "Person", "name" : "Fame", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/fame" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Building Oracle GoldenGate Cloud Service (GGS) with Terraform", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/ggs_deployment_page-3.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/building-oracle-goldengate-cloud-service-ggs-with-terraform", "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": "Initial Load from Tandem (HP-UX) to AWS Kafka", "mainEntityOfPage": "https://rheodata.com/en-us/blog/initial-load-from-tandem-hp-ux-to-aws-kafka", "datePublished": "10/11/2025", "dateModified": "10/11/2025", "articleBody": "Working with a customer where we needed to move data from a Tandem (HP-UX Guardian) system up to an AWS EC2 platform that will eventually end up on an AWS MSK Cluster (Kafka). The concept is pretty straight forward; however, I will say that putting it into practice provided to be a challenge. The biggest part of this challenge was the initial load process that should be used. Oracle GoldenGate is the best replication tool on the market, but the one thing that it lags in is the approach of initial load. The initial load process for Oracle GoldenGate (both Oracle and Heterogenous) comes in many different options. The documentation for initial loads have changed over the years as well as the approaches. With the latest release of Oracle GoldenGate (21c), this hasn’t gotten any better. But if you want a reference point, you can review the steps in Chapter 11 of the Using Oracle GoldenGate with Oracle Database (here). To perform this initial load from a Tandem system to Kafka, we used two different binary sets. In this case, we were using the following: Oracle GoldenGate 12c (12.2.0.1)(Classic) Oracle GoldenGate 21c (21.5.0.0)(Microservices) As you may have noticed, the implementation we were working with is a Classic to Microservices architecture. Oracle GoldenGate (Microservices) is the latest release of Oracle GoldenGate and the direction that Oracle is pushing for data integration strategies. Below you see the basic concept of this architecture. The Classic side of the architecture is straight forward when it comes to Oracle GoldenGate. When the architecture transitions to the EC2 side, Oracle GoldenGate (Microservices) has a few more moving parts that should be managed. For the purpose of the initial load, the only service that is needed is the Receiver Service (port 16003); more on this shortly. In the diagram above, we are only going to be discussing the items in red. These items represent the initial load process and how this process was accomplished between a Oracle GoldenGate (Classic) running on a Tandem (HP-UX Non-Stop) and loading data into Kafka using Oracle GoldenGate (Microservices). The “direct load” initial load process was followed for this configuration (this approach is not covered in the 21c docs…just an FYI). Tandem (HP-UX Non-Stop): First thing that needs to be done is configuring the Initial Load Extract on the Tandem side. The following parameter file was used: EXTRACT eil RMTHOST , MGRPORT 16003 RMTFILE TABLE *.*.*; This initial load extract looks pretty standard. We are telling Oracle GoldenGate (Classic) to read all the data from the tables that were in the TABLE line. Essenstally doing a “SELECT *” and pull all the data. Then move that data across the network to the remote server and begin writing to the remote file. Oracle GoldenGate (Microservices) begins writing the remote file, but then immediately presents a “broken pipe” error. This behavior caused a lot of confusion. After opening an SR and talking with Oracle resources, it was noted that the Tandem default settings for the buffer needed to be changed. Note: By default, Tandem sets its TCP/IP buffer to 64K. Apparently, using the default settings on the Tandem didn’t work. To work around the “broken pipe” issue, we had to set the TCPFlushBytes and TCPBufSize to less than 28K. This resulted in our extract parameter file being changed: EXTRACT eil TCPFlushBytes 27000 TCPBufSize 27000 RMTHOST , MGRPORT 16003 RMTFILE TABLE *.*.*; By adding the TCP parameters, the extract in Oracle GoldenGate (Classic) is able to successfully make the connection to the Oracle GoldenGate (Microservices): Receiver Service on port 16003 and write data to the remote file. By shrinking the TCPFlushBytes and the TCPBufSize below 28K, this avoids a known limitation with Oracle GoldenGate and the usage of RMTTASK and RMTFILE, since they both make the same calls. To build the extract within Oracle GoldenGat (Classic) the following commands were used: GGSCI> add extract eil, SOURCEISTABLE EC2 Instance/GoldenGate Microservices With the RMTFILE being written successfully to the AWS EC2 platform, an initial load replicat can be built to apply the bulk data to Kafka. The parameter file for the replicat is: REPLICAT ril TARGETDB LIBFILE libggjava.so SET property= SOURCEDEFS MAP *.*.*, TARGET *.*.*; Now the exact setting for configuring the connection to Kafka are contained within two properties files. These properties files are used to make the connect and what format the data should be provided in. The first of these files is the Kafka.properties (connection file). This file sets ups the Kafka Handler and any specific items that are needed for the handler. The example that we used is similar to the following: gg.handlerlist=kafkahandler #The handler properties gg.handler.kafkahandler.type=kafka gg.handler.kafkahandler.kafkaProducerConfigFile=kafka.properties gg.handler.kafkahandler.topicMappingTemplate=${toLowerCase[${tableName}]} gg.handler.kafkahandler.keyMappingTemplate=${toLowerCase[${tableName}]} gg.handler.kafkahandler.mode=op gg.handler.kafkahandler.Format=json goldengate.userexit.writers=javawriter javawriter.stats.display=TRUE javawriter.stats.full=TRUE gg.log=log4j gg.log.level=INFO gg.report.time=30sec gg.classpath=/app/orabd/opt/DependencyDownloader/dependencies/kafka_2.7.1/* sasl.jaas.config=org.apache.kafka.common.security.scram.ScramLoginModule required \ username=“” \ password=“”; security.protocol=SASL_SSL Next we defined a second properties file that defines the connection to Kafka and the associated brokers. As well as setting up the conversion of the data formats and performance tuning items. Our file looked similar to the following: # address/port of the Kafka broker bootstrap.servers= #JSON Converter Settings key.converter.schemas.enable=false value.converter.schemas.enable=false value.serializer = org.apache.kafka.common.serialization.ByteArraySerializer key.serializer = org.apache.kafka.common.serialization.ByteArraySerializer #Adjust for performance buffer.memory=33554432 batch.size=2048 linger.ms=500 After setting the properties file that will be used by Oracle GoldenGate for Big Data to connect to Kafka, we needed to add the replicat to the architecture. This can be done either from the AdminClient or from the HMTL5 web page through the Administration Service. For command line compatibility, the following steps are done through the AdminClient: AdminClient> add replicat ril, exttrail AdminClient> start replicat ril At this stage, after starting the replicat (RIL), we are reading the RMTFILE and performing a “direct load” initial load from Tandem to Kafka. With the “direct load” working, we were pushing approximate 5.4 million records in ~45 minutes. This was a single table load. Depending on the number of tables that need to be loaded, you will need to scale this approach with either more RMTFILEs or multiple replicats. Enjoy and happy replicating!", "image": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/tandem2bigdata-1-1.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" : "When IT executives evaluate Oracle workloads for Microsoft Azure migration, the conventional wisdom points toward SQL Server as the natural destination. However, this assumption overlooks a critical business reality: Oracle@Azure delivers superior ROI while preserving your technology investments. RheoData’s enterprise migration analysis reveals why Oracle@Azure represents the strategic choice for sustainable competitive advantage. Two Paths, One Clear Winner RheoData recently evaluated two distinct migration architectures for a client modernizing their Oracle infrastructure. The contrast illuminated fundamental differences that every IT leader should understand. Path 1: Oracle 19c on Azure VMs – The Short-Term Trap Traditional “lift and shift” approaches position Oracle 19c on Azure virtual machines with bi-directional replication capabilities. While this delivers immediate cloud benefits, it creates what RheoData identifies as “strategic debt” – investing in a platform with rapidly diminishing support runway. Path 2: Oracle@Azure with Oracle 23ai – The Growth Platform Oracle@Azure deploys Oracle Database 23ai through Oracle’s native cloud infrastructure directly within Azure. This architecture provides seamless integration between Azure services and Oracle’s most advanced database technology, delivering transformation rather than mere migration. The business case isn’t just technical – it’s about sustainable competitive advantage. The Oracle 19c Support Reality: A Ticking Clock Based on Oracle’s official Lifetime Support Policy (effective June 10, 2025), Oracle 19c presents significant timeline constraints: Premier Support ends December 2029 – Only four years of full support remaining from today Extended Support ends December 2032 – Seven-year limited runway with restrictions Java 8 support exclusion after December 2030 – Critical limitation for enterprise environments running integrated Java components Compare this to Oracle 21c (Innovation Release), which offers Premier Support only until July 2027 with no Extended Support available. The pattern is clear: investing in any Oracle release except 23ai means planning replacement before achieving full ROI. RheoData’s analysis shows that organizations choosing Oracle 19c today will face another migration decision within 3-4 years – creating compounded migration costs and technical debt accumulation. Oracle 23ai: Purpose-Built for AI-Powered Enterprise Success Oracle Database 23ai delivers over 300+enterprise-grade features designed for competitive differentiation. RheoData highlights the transformational capabilities that drive measurable business outcomes: AI Vector Search integrates semantic search across documents, images, and unstructured data with your private business information – no data movement required, maintaining security while unlocking insights. JSON Relational Duality eliminates the traditional document versus relational trade-off by providing unified access through both SQL and JSON APIs, accelerating application development cycles. Model Context Protocol (MCP) Integration enables direct AI assistant integration with built-in security, allowing agents to generate and execute SQL queries while maintaining enterprise governance. Oracle True Cache delivers automatically managed, in-memory SQL and key-value caching that accelerates application performance without complex configuration overhead. These aren’t incremental improvements – they’re foundational capabilities that position organizations for the next decade of data-driven competition. Why Oracle@Azure Wins the Total Cost Analysis RheoData’s comprehensive migration assessment reveals the hidden costs of Oracle-to-SQL Server conversion that impact bottom-line results: The 80/20 Reality of Database Migration While data type mappings appear straightforward, they represent only 15-20% of total migration effort. The remaining 80% includes: PL/SQL packages, procedures, and functions (60% of effort) – Complete code reconstruction required Application integration changes (20% of effort) – Connection strings, drivers, query modifications Triggers and complex constraints (10% of effort) – Logic restructuring across systems Performance optimization (10% of effort) – Platform-specific tuning requirements Hidden Cost Multipliers SQL Server migration introduces expense categories that Oracle@Azure eliminates: Development costs: 6-12 months of specialized conversion effort Risk mitigation costs: Extended parallel operations increase infrastructure expenses Retraining investment: DBA and developer education for new platform expertise Opportunity costs: Team focus diverted from strategic initiatives during extended migration period Oracle@Azure Value Acceleration RheoData’s clients achieve immediate ROI through preserved investments: Zero application rewrite – Existing PL/SQL code base remains fully functional Retained expertise – Current team skills continue delivering value Maintained performance characteristics – No unknown optimization requirements Preserved business logic – Critical processes require no reconstruction Strategic Positioning for Long-Term Success Oracle@Azure provides the enterprise foundation your organization needs for sustained competitive advantage: Cloud-native architecture through Oracle’s deep Azure integration delivers modern infrastructure benefits without platform migration risks. AI-driven capabilities position organizations at the forefront of data-driven decision making, with built-in security and governance. Innovation pipeline access ensures continuous competitive differentiation through Oracle’s ongoing AI and database technology investments. Enterprise-grade reliability combines Oracle’s proven database technology with Azure’s global infrastructure for maximum uptime and performance. RheoData’s Strategic Recommendation The data tells a compelling story: Oracle 19c represents a short-term fix when your organization requires long-term strategic advantage. With only four years of Premier Support remaining and Java 8 limitations creating operational constraints, this path misaligns with sustainable operational excellence. Oracle 23ai via Oracle@Azure delivers the platform foundation necessary for competitive differentiation over the next 5-10 years. This approach provides true digital transformation – not just infrastructure migration – positioning your organization for sustained success in an AI-powered marketplace. The Bottom Line for Executive Decision-Making When evaluating Oracle migration options, consider this strategic question: Does your organization want to invest in a solution requiring replacement planning during implementation, or one that accelerates competitive advantage for the next decade? RheoData recommends proceeding with an Oracle@Azure proof of concept to validate performance characteristics and cost structures for your specific workloads. This strategic approach balances innovation with risk management while maintaining business continuity and maximizing technology investment returns. Contact RheoData at cloud@rheodata.com to discuss how Oracle@Azure can accelerate your organization’s digital transformation objectives while eliminating the risks and costs associated with heterogeneous database migration.", "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 Azure Migration: Oracle@Azure vs. SQL Server – The Strategic Choice That Drives Results", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/Gemini_Generated_Image_5dnwx15dnwx15dnw-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-azure-vs-sql-server-migration", "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" : "News on the Oracle GoldenGate front!!!!! Over the years, Oracle has taken the Oracle GoldenGate product and have used it for a wide range of use-cases. In 2017, Oracle introduced the world to the next evolution of Oracle GoldenGate with the release of Oracle GoldenGate Microservices in the 12.3.0.1 version. At the time, I had the privilege of shepherding in the this new architecture and truly believe it is moving the right direction for high-volume replication, hub-n-spokes architectures, and data mesh frameworks. At the same time, what I was witnessing and was a bit concern about is the close integration between the Oracle database and the Oracle GoldenGate product. Back in 2013, I wrote about Advanced Replication and Streams being dead due to Oracle Database documentation mentioning that the direction for replication was changing towards Oracle GoldenGate. Fast forward a few years … when I woke up this morning and scrolling through LinkedIn, some associates were posting information on Oracle Database 21c New Features. And the transition to full Oracle Database integration has begun …. In the new features doc for Oracle Database 21c, Oracle GoldenGate can be found under “Performance and High Availability”. Under this category, there are features related to the following: Automatic CDR Enhancements Improved Support for Table Replication for Oracle GoldenGate LogMiner Views Added to Assist Replication Oracle GoldenGate for Oracle and Stream Support for JSON Data Type These features are only the start of what should be seen as the Oracle GoldenGate product becomes more and more integrated into the Oracle Database. The real questions is going to be around the heterogenous nature of Oracle GoldenGate. With the integration of the Oracle side of the product moving into the Oracle Database, will Oracle GoldenGate eventually drop the heterogenous functionality of the product? I don’t think so … that would be contradictory to their marketing of a “data mesh” framework and pushing customers into that model. What I would expect to see is more of the microservices architecture coming to the heterogenous platforms and even Big Data. This would align beautifully with the “data mesh” marketing and allow for a single point of reference with multiple product offerings. Oracle is moving the Oracle GoldenGate product to be closer related with the Oracle Database and keeping the heterogenous nature of the product. Only time will tell if merging into the Oracle database is going to benefit the product and Oracle. Just my 2 cents… Enjoy!!!", "author" : { "@type" : "Person", "name" : "rheostage5132", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/rheostage5132" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "The transition has begun … Oracle GoldenGate merging into Oracle Database 21 (new features)", "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/the-transition-has-begun-oracle-goldengate-merging-into-oracle-database-21-new-features", "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" } } ```