--- title: RheoData Blog | Business Insights description: Business Insights | RheoData Blog Posts --- [![RheoData - Logo - transparent-1](https://rheodata.com/hs-fs/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png?width=250&height=50&name=RheoData%20-%20Logo%20-%20transparent-1.png "RheoData - Logo - transparent-1")](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - 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Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - [Exadata & Oracle Database](https://rheodata.com/exadata-and-oracle-database-26ai) - Verticals - [Manufacturing](https://rheodata.com/manufacturing) - [Retail](https://rheodata.com/retail) - [State & Local](https://rheodata.com/sled) - [Customer Stories](https://rheodata.com/customer-stories) - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration) - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) Posts about # Business Insights ## [Stop Managing Port Numbers. Start Managing Data.](https://rheodata.com/en-us/blog/eliminate-goldengate-port-management) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 18, 2025 9:57:17 AM Your senior DBA just spent 20 minutes trying to remember which port number connects to the... [CONTINUE READING](https://rheodata.com/en-us/blog/eliminate-goldengate-port-management) ## [GenAI: How to get started](https://rheodata.com/en-us/blog/genai-getstarted) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:31:00 PM This post we are going to look at some items related to getting started with Generative AI. This is... [CONTINUE READING](https://rheodata.com/en-us/blog/genai-getstarted) ## [Oracle GoldenGate 23ai Performance Tuning: Achieving 10M Rows/Hour on X-Small Snowflake Warehouses](https://rheodata.com/en-us/blog/oracle-goldengate-performance-settings-cut-snowflake-costs) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:57 PM Your Snowflake bills are probably 3x higher than they need to be. We wrapped up an implementation... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-goldengate-performance-settings-cut-snowflake-costs) ## [RheoData achieves Service Expertise in Oracle GoldenGate](https://rheodata.com/en-us/blog/rheodata-achieves-service-expertise-in-oracle-goldengate) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:56 PM Three and a half-years after our owner/founder left Oracle; RheoData has earned the Oracle Service... [CONTINUE READING](https://rheodata.com/en-us/blog/rheodata-achieves-service-expertise-in-oracle-goldengate) ## [MySQL 8.0 and beyond: Lifecycle Support](https://rheodata.com/en-us/blog/mysql-8-0-and-beyond-lifecycle-support) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:53 PM MySQL has long been the number two database in the world! Initially MySQL started as one of the... [CONTINUE READING](https://rheodata.com/en-us/blog/mysql-8-0-and-beyond-lifecycle-support) ## [Harnessing the Power of AI and Machine Learning with RheoData: A Path to Success](https://rheodata.com/en-us/blog/harnessing-ai) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:48 PM In the dynamic landscape of modern technology, few fields hold as much promise and potential as... [CONTINUE READING](https://rheodata.com/en-us/blog/harnessing-ai) ## [Oracle to GCP Migration: Oracle@GCP vs. Cloud SQL – The Strategic Choice That Drives Results](https://rheodata.com/en-us/blog/oracle-gcp-vs-cloud-sql-migration) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:47 PM The partnership between Oracle and Google Cloud represents one of the most significant... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-gcp-vs-cloud-sql-migration) ## [Hyper Data Ingestion with Oracle GoldenGate Service (GGS)](https://rheodata.com/en-us/blog/hyper-data-ingestion-with-oracle-goldengate-service-ggs) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:45 PM Data ingestion is always the beginning stages of getting data into a data warehouse and/or cloud.... [CONTINUE READING](https://rheodata.com/en-us/blog/hyper-data-ingestion-with-oracle-goldengate-service-ggs) ## [Performance with Oracle GoldenGate](https://rheodata.com/en-us/blog/performance-with-oracle-goldengate) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:45 PM [CONTINUE READING](https://rheodata.com/en-us/blog/performance-with-oracle-goldengate) ## [The Oracle GoldenGate HA Gap: Your Data Can’t Wait for Oracle to Catch Up](https://rheodata.com/en-us/blog/goldengate-ha-gap) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:30:43 PM Oracle GoldenGate doesn’t have a native high availability solution. For a tool that’s... [CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-ha-gap) ### 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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Again. Your new team member has a bookmark folder with 12 different Oracle GoldenGate URLs. Each with a different port. Half of them don’t work anymore because someone changed the configuration last month. And your security team just sent another email. Another quarterly audit. Another spreadsheet asking you to justify why your GoldenGate Microservices deployment has 15 ports exposed to the network. This is what Oracle GoldenGate 23ai Microservices Architecture gives you: incredible power for real-time data replication. And a port number management nightmare that slowly drives your team insane. I know because I’ve sat in 30 different conference rooms/conversations this year. Same whiteboard diagram. Same conversation. Smart IT leaders pointing at their GoldenGate architecture explaining which microservice runs on which port. And why their DBAs spend more time managing access than managing data. Here’s what nobody tells you when you deploy GoldenGate Microservices: The microservices work great. The port management? That’s the hidden tax you’ll pay every single day. The Port Number Tax: What It’s Really Costing You Let me paint a picture from a $2.8 billion retail operation we worked with recently. Brilliant IT team. Oracle GoldenGate 23ai running perfectly. Real-time replication working like clockwork. But they had this massive three-ring binder. I’m not kidding. A physical binder. Titled “GoldenGate Access Guide.” Inside? Service Manager: Port 9100 Administration Server: Port 9101 Distribution Server: Port 9102 Receiver Server: Port 9103 Performance Metrics Server: Port 9104 And that’s just deployment one. They had four deployments. Each with its own set of ports. Each requiring documentation. Each demanding that DBAs remember or look up which number goes where. Their lead DBA told me something I’ll never forget: “I spend more time explaining port numbers to new team members than I spend teaching them about GoldenGate replication.” Think about that. Oracle built this incredible technology for real-time data integration. And the best talent in the organization is teaching port number memorization. That’s not a training problem. That’s a port number tax. And you’re paying it every single day. What Your Team Actually Needs When I talk to CIOs and VPs of IT about Oracle GoldenGate Microservices deployments, the port number frustration comes up every time: First frustration: “My DBAs waste hours managing port number documentation instead of optimizing our data pipelines.” Your team knows Oracle GoldenGate inside and out. They understand replication topology. They can troubleshoot lag issues in their sleep. They’re experts at what matters. But they’re spending time maintaining wiki pages, updating spreadsheets, and answering Slack messages about which port connects to which service. That’s not why you hired them. Second frustration: “Every security audit turns into a port number interrogation.” Your quarterly security review always includes the same conversation: “Why are ports 9100 through 9115 exposed?” “Why does each microservice need its own port?” “Can’t we consolidate this?” You explain that GoldenGate Microservices Architecture works this way by design. You explain that each service needs its own port. You explain that it’s all necessary. And the security team documents another finding. Another item requiring justification. Another complexity to explain next quarter. Third frustration: “New team members take weeks to learn our access patterns.” Onboarding a new DBA or integrating a contractor should take days, not weeks. But when someone joins your team, they need: A bookmark folder with 15+ URLs Documentation explaining which service lives on which port Training on your specific deployment naming conventions A cheat sheet they’ll refer to for months That’s not onboarding. That’s port number orientation. And it delays the real work by weeks. The Solution: Eliminate Port Numbers From Your Team’s Vocabulary Here’s what changes when you implement NGINX reverse proxy for Oracle GoldenGate: Your entire team accesses everything through one URL. No port numbers. No memorization. No documentation to maintain. Just: https://goldengate.yourcompany.com Service Manager? Same URL. Distribution Server? Same URL. Performance Metrics? Same URL. The reverse proxy handles routing. Your team handles data replication. Your security team sees one exposed port. Not 15. Not 12. Not even 5. One. Port 443. Standard HTTPS. Everything else runs internally, protected behind your reverse proxy. Every security audit gets simpler. Every compliance review gets shorter. Every risk assessment shows improvement. Your onboarding time drops from weeks to hours. New team member? Give them one URL. One set of credentials. Done. They’re productive on day one because they’re not memorizing your port number scheme. Why RHEL 8 Teams Hit a Specific Challenge If you’re running RHEL 8 or Oracle Linux 8, you’ve probably discovered that the default NGINX installation doesn’t meet Oracle GoldenGate 23ai requirements. The default NGINX 1.14 stream won’t work. You need 1.19.4 or higher. And nobody tells you this until you’re troubleshooting connection failures at midnight. We’ve walked 13 teams through this exact scenario. The fix takes mear minutes when you know what to do: # Reset module configuration dnf module reset nginx # Enable the right stream dnf module enable nginx:1.20 # Install NGINX dnf install nginx But discovering you need to do this? That’s cost teams days of troubleshooting. The RheoData Difference: We’ve Done This Before Here’s what we bring to Oracle GoldenGate reverse proxy implementations: We know the gotchas. RHEL 8 module streams. SSL cipher configurations. Certificate chain requirements. The specific settings Oracle’s ReverseProxySettings utility needs. We’ve documented every one through 30+ implementations. We understand your constraints. You can’t take GoldenGate offline during business hours. Your security team needs specific cipher suites. Your monitoring tools need to integrate. Your DBAs need training on the new access patterns. We plan for all of it. We deliver in phases. Quick win in 90 days: Your team gets simplified access and you get your first full night of sleep. Six months: Your security posture improves measurably. Twelve months: You’ve converted infrastructure complexity into a competitive advantage. What Success Looks Like Three months after implementing reverse proxy for that $2.8 billion retailer, here’s what changed: That three-ring binder? Gone. The lead DBA recycled it during an office cleanup. Nobody even noticed it was missing because nobody needed it anymore. Their port number documentation? Deleted. Security audit findings related to exposed services? Disappeared. Time spent onboarding new DBAs? Cut from three weeks to three days. But the real win? Their senior DBA told me: “I finally have time to work on the replication optimization project we’ve been delaying for a year. We’re not managing port numbers anymore. We’re managing data again.” That’s what happens when you eliminate infrastructure friction. Your team remembers why they chose database engineering in the first place. The Investment: Time and Expertise Implementing NGINX reverse proxy for Oracle GoldenGate takes expertise. Not just in NGINX. Not just in GoldenGate. But in how IT operations actually work. You need someone who understands: Why your GoldenGate deployment must stay online during implementation How your security team evaluates cipher configurations What your DBAs need to remain productive during the transition Where your monitoring tools integrate with the new architecture You could spend six months building this expertise internally. Watching videos. Reading documentation. Troubleshooting RHEL 8 module streams at 2 AM. Or you could work with a team that’s done this time and time again and knows exactly how to avoid every pitfall. Your Next Step If you’re tired of infrastructure complexity stealing time from strategic initiatives, let’s talk. Not a sales pitch. A real conversation about your Oracle GoldenGate environment. Your team’s pain points. Your security requirements. Your timeline constraints. We’ll tell you honestly if reverse proxy makes sense for your situation. And if it does, we’ll show you exactly how we’d implement it without disrupting your operations. Schedule a 30-minute GoldenGate infrastructure assessment. No cost. No obligation. Just an experienced perspective on your specific challenges. Because you didn’t become CIO to manage port documentation. You became CIO to drive business value through technology. Let’s make your infrastructure work that way. Why Partner With RheoData? Oracle GoldenGate Expertise: We’ve implemented GoldenGate solutions across hundreds of organizations, managing everything from real-time replication to cloud migration. Retail Industry Focus: We understand your seasonal demands, peak period requirements, and zero-downtime mandates because we work exclusively with retail IT operations. Managed Service Approach: We don’t just implement and leave. We monitor, maintain, and optimize your GoldenGate infrastructure so your team can focus on business initiatives. Proven Methodology: Our phased approach delivers quick wins while building toward comprehensive infrastructure modernization. Ready to simplify your GoldenGate access?", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "18/11/2025", "datePublished" : "18/11/2025", "headline" : "Stop Managing Port Numbers. Start Managing Data.", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/Gemini_Generated_Image_swtwdrswtwdrswtw.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/eliminate-goldengate-port-management", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "This post we are going to look at some items related to getting started with Generative AI. This is mostly related to how I got started looking at GenAI and what I presented this year at the Rocky Mountain Open User Group (RMOUG). The presentation that I presented on provided details on what you should look forward and understand when talking about GenAI. I broke this topic down into three distinct sections: Artificial Intelligence How you can get started Possibilities Now, I cannot go into all the details of these section in this blog post, but there was a lot of good information handed out during this session. Artificial Intelligence Many people didn’t realize that the concept and terms related to Artificial Intelligence has been out since the Birth of AI (1950–1956). It has gone through many different stages to reach where it is now with General AI (2012-Present). The image below shows a rough timeline on what these stages have been and were: Over the last two years, AI has exploaded across every industry. Yet, many don’t see how and where AI is being used within their everyday spectrum. AI has be placed in every type of application you can think of. Microsoft has place in their productivity suite with the release of Copilot ($$/user/mo). Google has done the same thing with Gemini in Google Workspace (included in cost based on package). Oracle has embedded AI into its products, mostly applications, but leverage Cohere to do this. Apple was one of the first out with “siri” as a digital assistant, which many use today. There are many different ways that AI is being used within our every day lives, yet so many do not realize it. As you can see, AI is or can be built into any thing that is useful. How do you get started? There are four basic things needed in order to get started. During the presentation I talked about these four items in terms of personal growth. After all, that is what you need to move forward if you really want to understand AI. These four things that you need in order to understand AI, at a deeper layers is: A desire to learn Reading a lot of documentation, I mean a lot. A set of tools Large Language Models (LLM) A desire to learn With AI being such a new thing to many, although it has been around for 75 years, getting a good understanding of what AI is and how it can be useful is key. This, in my opinion, can only be done if you have a desire to learn and be uncomfortable for a short period of time. Many vendors out there, Microsoft, Google, and Oracle, are trying to make it easier for you. Yet at the same time, if you only know the top layer, you’ll never know how it truly works. You need to spend time digging deep into the topic. Which brings you to the next bullet point: Documentation All of the AI providers provide a lot of documentation. So much that is is confusing when you first start digging into them. For Microsoft, you need to read the OpenAI docs. For Oracle, Cohere. For Goolge, Gemini. I’ll let you in on a little secret thought, they are all pretty much the same. If you can learn one, you can learn all of them. Additionally, the concepts bleed over into the just about every GUI interface provided with vendors. The concept of “notebooks” which is referenced a lot if really just a development platform to introduce AI through python concepts. Keep reading. Keep writing. The read some more. Tools When it comes to tools, there are a lot of tools on the market that you can use. The hyperscalers provide tools through their cloud platform interfaces. There are third party tools like pycharm or VSCode that are good for writing python code to interact with AIs. Many of the AIs provide Application Programming Interfaces (APIs) that allow you to interact with the AI or tie external applications into the AI. These are very powerful approaches to making AI do some cool stuff. Lastly, the ability to just talk to AI in “natural language” makes the concepts even more intergruging to use. Who doesn’t like just being able to ask a question and get an answer back? Large Language Models (LLMs) The last tool you should know about are Large Language Models (LLMs). Many hear the term but don’t quite understand it. LLMs are models that are trained on a per-determined set of data. Some models are smaller than others, while the larger ones are trained on millions if not billions of tokens. What is a token? The term token has been explained in multiple ways. Some people think a token is a single “word” or “phrase” that is given and retured by the LLM. The best explination I’ve heard and made sense was by provided Google. A “token” is a “character and a half”. Is that actually correct? Not sure, but it makes sense when you start looking at the cost factor of running your queries against an LLM. Concepts With all the items listed in the previous section, there are concepts that have to be understood as well. These concepts are: Fine-Tuning Embedding Grounding Understanding these concepts, you’ll be able to start development some LLMs that make sense for you to use and build from. Fine-Tuning This is the process of “teaching” the LLM what you want it to know. This is like a student learning a specialized task to make their job easier. In order to tune an LLM, you would need to provide it a JSONL document that consists of a conversation like input, with questions and answers. Then the LLM can learn and you can ask it generalized questions around the topic it was trained on. The JSONL document that you provided should follow the 80/20 rule. 80% of the questions are used to train the LLM while the last 20% of of the questions are reserved to validate the training. Embedding Embedding is another concept; however, it makes a lot of sense when you are building applications like Vector Searches. The embedding process uses a specialized LLM that will convert your unstructured data into a “vector” that is stored in a database. Then this information is used to retrieve data based on “semantic” search. What does this mean? It means that your input is converted to a “vector” string and then compared to the “vector” stored in the database. All records returned, following a mathematical equation, are within a given range of the vector. This is known as the “top-K” approach. Grounding Grounding is the approach of providing relavant information to the LLM without training. It is an approach to elevate the halluination of the LLM when returning results. This approach is also known a “Retrieval Augmented Generation (RAG)”. By grounding an LLM, you care ensuing the context of the results are what is needed for the question being asked. The also leads you into the concept of “specialized” LLMs. An LLM becomes specialized when you train the LLM for a specific task and then provide context with external data sources. Agents Agents are the next evolution of the AI ecosystem. Although the General AI approach has only been around for two years, agents came about very quickly. Agents are autonomous entities that leverage AI to perfrom tasks. Some agents perform single tasks while some provide mulitiple tasks. At the same time, agents are goal-oriented and capable of making decisions on their own. Additionally, they can interact with other agents within their environments or outside their environments. Some of the most common agents are chatbots, virtual assistants, or gaming characters. AI Swarms This is a new concept that has been around for about six-months. The concept of a “swarm” is the ability to have an agent manage multiple agents. This greats a “swarm” of agents that can be interacted with and provide services — enabling a “hive mind” approach between the agents. Using AI swarms increase efficiency and effectivness through collabrative problem-solving. Yes, to be effective, it requires careful management of communication and coordination between agents. Summary In this post, I walked you through what I looked at when I was getting stared with GenAI. I provided highlights of all the concepts needed to build and use an AI/LLM. Lastly, I provided minor details on Agents and AI Swarms which are fairly new to the AI ecosystem and are forcing many to start looking at AI different.", "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" : "GenAI: How to get started", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_335969170-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/genai-getstarted", "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" : "Your Snowflake bills are probably 3x higher than they need to be. We wrapped up an implementation where we slashed compute costs by 70% while improving replication performance by 67%. The secret? Properly configured Oracle GoldenGate 23ai performance settings specifically tuned for X-Small Snowflake warehouses. Here’s the reality – most organizations start with Medium or Large Snowflake warehouses for Oracle-to-Snowflake replication because they’re afraid of performance issues. That fear costs them $40,000+ annually in unnecessary compute charges. We need results, not expensive insurance policies. Today, I’m sharing the critical settings that make this possible. The $40K Question: Why X-Small Works Before diving into configuration, let’s address the elephant in the room. An X-Small Snowflake warehouse has: 1 compute cluster 8 credits/hour consumption Processes ~16M rows/hour (properly configured) Compare that to a Medium warehouse at 32 credits/hour, and you’re looking at 4x the cost for maybe 2x the performance. The math doesn’t work. Extract Configuration: Where Performance Begins Your Extract process sets the foundation for downstream performance. Here’s the configuration that’s achieved 67% performance improvements: EXTRACT EXT_SNOW USERIDALIAS GGADMIN_ORCL DOMAIN OracleGoldenGate EXTTRAIL sn SOURCECATALOG PROD_PDB -- Critical performance optimizations TRANLOGOPTIONS INTEGRATEDPARAMS (MAX_SGA_SIZE 2048, PARALLELISM 4) NOCOMPRESSUPDATES -- Include monitoring heartbeat TABLE GGADMIN.GG_HEARTBEAT; -- Business tables TABLE SALES.ORDERS; TABLE SALES.ORDER_ITEMS; TABLE INVENTORY.PRODUCTS; TABLE INVENTORY.MOVEMENTS; Key Performance Settings Explained: MAX_SGA_SIZE 2048 – Allocates 2GB of memory for LogMiner operations. This prevents constant memory allocation/deallocation that kills performance. We’ve seen 40% improvement with this setting alone. PARALLELISM 4 – Enables 4 parallel LogMiner processes. This setting improved extraction rates from 6M to 10M rows/hour in production environments. NOCOMPRESSUPDATES – Critical for Snowflake targets. Compressed updates require additional processing on the Snowflake side. Eliminating compression reduced apply time by 23%. Distribution Path: Don’t Bottleneck Here The distribution configuration often gets overlooked, but incorrect settings here negate all upstream optimizations: { name: “PATH_TO_SNOW”, source: “EXT_SNOW”, target: {   host: ogg-da-server”,   port: 9103,   trail: “sn” }, compressionType: “LZ4”, encryptionType: “AES256”, tcpBufferSize: 65536 } compressionType: LZ4 – Provides 3:1 compression with minimal CPU overhead. GZIP gives better compression but increases latency by 35%. tcpBufferSize: 65536 – Larger buffer sizes reduce network round trips. This setting alone improved throughput by 18% over WAN connections. Replicat Configuration: Where X-Small Shines The Replicat configuration determines whether your X-Small warehouse keeps up or falls behind: REPLICAT RSNOW REPORTCOUNT EVERY 30 MINUTES, RATE GROUPTRANSOPS 10000 MAXTRANSOPS 20000 -- Map tables MAP GGADMIN.GG_HEARTBEAT, TARGET GGADMIN.GG_HEARTBEAT; MAP SALES.ORDERS, TARGET ANALYTICS.ORDERS; MAP SALES.ORDER_ITEMS, TARGET ANALYTICS.ORDER_ITEMS; GROUPTRANSOPS 10000 – Groups up to 10,000 operations into a single transaction. This reduces Snowflake transaction overhead by 85%. MAXTRANSOPS 20000 – Forces a commit at 20,000 operations. Prevents memory bloat while maintaining performance. Snowflake Event Handler: The Secret Sauce This is where most implementations fail. The Snowflake Event Handler properties make or break X-Small warehouse performance: # Snowflake Event Handler Configuration gg.handlerlist=snowflake gg.handler.snowflake.type=snowflake gg.handler.snowflake.mode=op # Authentication gg.eventhandler.snowflake.connectionURL=jdbc:snowflake://{ID}.snowflakecomputing.com/?warehouse=COMPUTE_WH&db={DATABASE} # CRITICAL: In-Memory Operation Aggregation gg.aggregate.operations=true gg.aggregate.operations.flush.interval=30000 # SQL-based aggregation for massive performance gg.aggregate.operations.using.sql=true # Uncompressed updates for MERGE operations gg.compressed.update=false # Use MERGE instead of DELETE+INSERT gg.eventhandler.snowflake.deleteInsert=false # Handle large objects efficiently gg.maxInlineLobSize=24000000 # JVM optimization jvm.bootoptions=-Xmx8g -Xms8g Performance Impact of Each Setting: gg.aggregate.operations=true with flush.interval=30000 – Batches operations for 30 seconds before applying. Reduces Snowflake API calls by 95%. gg.aggregate.operations.using.sql=true – This is the game-changer. Aggregates operations at the SQL level, reducing data movement by 60%. gg.compressed.update=false – Required for MERGE operations. Compressed updates force DELETE+INSERT operations which are 3x slower. gg.eventhandler.snowflake.deleteInsert=false – Enables native MERGE SQL. Improves update performance by 250% on X-Small warehouses. jvm.bootoptions=-Xmx8g -Xms8g – Allocates 8GB heap. Prevents garbage collection pauses that cause apply lag. The Snowflake Warehouse Configuration Don’t forget to optimize the Snowflake side: ALTER WAREHOUSE COMPUTE_WH SET   WAREHOUSE_SIZE = 'X-SMALL’   AUTO_SUSPEND = 60   AUTO_RESUME = TRUE   MIN_CLUSTER_COUNT = 1; AUTO_SUSPEND = 60 – Suspends after 1 minute of inactivity. With proper batching, saves 70% on compute costs. Real-World Performance Metrics With these configurations, here’s what we’ve achieved on X-Small warehouses: Initial load: 100M rows in 6 hours Change data capture: 10M changes/hour sustained Replication lag: < 60 seconds average Monthly cost: $1,200 vs. $4,000 on Medium warehouse Common Mistakes That Kill Performance Using compressed updates with MERGE – Increases apply time by 300% Small flush intervals (<30 seconds) – Creates excessive Snowflake transactions Insufficient JVM memory – Causes GC pauses and lag spikes Missing SQL aggregation – Processes each row individually Wrong compression algorithm – GZIP adds 35% latency The Bottom Line Every organization processing less than 50M daily changes can run on X-Small Snowflake warehouses – if configured correctly. The settings I’ve shared have been battle-tested across implementations processing billions of rows. Stop accepting massive Snowflake bills as “the cost of doing business.” With proper Oracle GoldenGate configuration, you get: 70% reduction in Snowflake compute costs 67% improvement in extraction performance Sub-minute replication lag 99.9% reliability These aren’t theoretical numbers. They’re production results from organizations that decided expensive wasn’t better. Your Next Steps The configurations in this post are your starting point. Every environment has unique characteristics that require tuning. But if you’re running Medium or Large warehouses for standard Oracle-to-Snowflake replication, you’re leaving money on the table. Ready to cut your Snowflake costs while improving performance? The team at RheoData specializes in Oracle GoldenGate optimizations that deliver measurable ROI. We don’t just talk about transformation – we deliver it, measure it, and accelerate it. Contact RheoData for a performance assessment or schedule a consultation to discuss your specific environment. — cloud@rheodata.com Remember: In the world of real-time replication, performance and cost efficiency aren’t mutually exclusive. They’re complementary when you know which knobs to turn.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Oracle GoldenGate 23ai Performance Tuning: Achieving 10M Rows/Hour on X-Small Snowflake Warehouses", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1499606969-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-goldengate-performance-settings-cut-snowflake-costs", "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" : "Three and a half-years after our owner/founder left Oracle; RheoData has earned the Oracle Service Expertise in North America for Oracle GoldenGate. The accreditation was awarded in North American but we continue support you around the globe. RheoData is now positioned to bring quality Oracle GoldenGate on-premise and cloud services and licenses to our customers. GoldenGate Services we offer: Architecture review and health-checks Design and/or re-architect Health-checks on database and replication Migrate to the cloud (AWS, Azure, GCP with on-premise/OCI with BYOL or Cloud-base licenses) Oracle GoldenGate Implementation Oracle GoldenGate for Oracle Non-Oracle (Heterogenous) Big Data Oracle GoldenGate Managed Services Keep your environment running at peak performance – on-premise or cloud without the OpEX overhead Oracle GoldenGate Modernization Upgrade from Classic to Microservices Understanding of Microservices We help our customers achieve maximum performance with Oracle GoldenGate, on-premise and cloud while enabling their data to move in real-time at breakneck speed. RheoData has deep experience moving from Classic to Microservices architecture with virtually no interruption to ongoing operations. Here are a few of our use cases and customer success stores illustrating our capabilities: Shoe Carnival – Zero-downtime migration from Oracle GoldenGate (Classic) to Oracle GoldenGate (Microservices) (here) Altec – Hyber Data Ingrestion – OCI and GGS use case (here) Tire Manufacture Distributor – Oracle GoldenGate for Big Data to Google Cloud Storage (here) Migrate data to ADW – Oracle GoldenGate to Autonomous Data Warehouse (here) Additional GoldenGate consulting services aligned with our expertise, including: Data Integration, Consolidation, and Data Warehousing Query offloading for Reporting processes Bi-Directional/Multi-Master replication for high-volume transaction architectures Disaster Recovery Testing Real-Time Data Replication Big Data integration using Oracle GoldenGate to AWS S3 Big Data integration using Oracle GoldenGate to Kafka Big Data integration using Oracle GoldenGate to Snowflake Big Data integration using Oracle GoldenGate to Google Cloud Storage Oracle to Oracle Replication Oracle to MySQL Replication Oracle to Postgres Replication Microsoft SQL Server to Oracle Replication Microsoft SQL Server to MySQL Replication PostgreSQL to Oracle Replication PostgreSQL to PostgreSQL Replication Cloud integration with Amazon AWS Cloud integration with Google Cloud (GCP) Cloud integration with Azure Cloud integration with Oracle Cloud Infrastructure (OCI) Oracle GoldenGate Service (OCI based Oracle GoldenGate) Replication integration with REST API framewor We look forward to connecting with you on all your project, infrastructure, and performance needs, especially Oracle GoldenGate! Contact us today email (hello@rheodata.com) or by phone at +1 (678) 608-1352 x 102 or 103. How can we be helpful today?", "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" : "RheoData achieves Service Expertise in Oracle GoldenGate", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/rheodata_GG-Service-Badge-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/rheodata-achieves-service-expertise-in-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" : "MySQL has long been the number two database in the world! Initially MySQL started as one of the first open source databases that the world used as an alternative to Oracle and Microsoft’s SQL Server, so much so that sometime in 2009 Oracle purchased the Sun Microsystems which was the parent company for MySQL. Throughout the years, MySQL has been a side play for the Oracle sales teams and many organizations overlooked MySQL; until recently. Back in 2018, Oracle announces the latest revision of MySQL with the release of MySQL 8.0. Since that time, Oracle and the MySQL team has made constant improvments to MySQL through incremental released. Recently, the MySQL team has release 8.0.33. Although this post is not intended to be a feature release, knowing that the MySQL team at Oracle is consistantly improving on MySQL is huge. With the consistent improvements to MySQL, Oracle has started to release its support lifecycle for this product. The long term release of MySQL 8.0 (8.0.33), there is a few dates that need to be kept in mind: Premier Support (PS) ends on 01 April 2025 Extended Support (ES) ends on 01 April 2026 Sustaining Support (SS) will not be available after 01 April 2026 These dates can be seen in the chart below as well. All this becomes important due to the fact that many organizations who are running MySQL are either running the MySQL 5.7 Community Edition or MySQL 5.7 Enterprise Edition. Both of which will be ending Extended Support in October of 2023, leaving companies with a decision on where to go next with their MySQL implementation. Where we can help? RheoData are the experts in helping organizations move mission critical database workloads between versions of MySQL Database. Wether your organization is considering moving on-premises to on-premises or looking to do a lift-n-shift to the cloud, maintaining operational readiness is the key to a successfully migration! RheoData experts can help you evaluate, plan, and implement a data integration/migration strategy to successfully build for the future! Get in touch today to build your migration strategy! —> sales@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" : "MySQL 8.0 and beyond: Lifecycle Support", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/mysql_database_lifecycle_support-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/mysql-8-0-and-beyond-lifecycle-support", "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 the dynamic landscape of modern technology, few fields hold as much promise and potential as Artificial Intelligence (AI) and Machine Learning (ML). These innovative technologies have revolutionized industries across the globe, from healthcare to finance, manufacturing to entertainment. The demand for AI and ML expertise is soaring, driven by the quest for efficiency, insights, and competitive advantage. In this fast-paced era, companies striving to stay ahead of the curve recognize the importance of integrating AI and ML into their operations. Whether optimizing processes, personalizing user experiences, or predicting future trends, AI and ML have become indispensable tools for innovation and growth. However, navigating the complexities of AI implementation requires specialized knowledge and expertise. This is where RheoData, as experts in Data Integration, ML, and AI, emerges as a crucial partner in building successful AI projects. The Rising Demand for AI and Machine Learning The digital transformation sweeping across industries has fueled the exponential growth of AI and ML. Organizations increasingly leverage these technologies to unlock value from vast amounts of data, automate tasks, and gain actionable insights. According to industry reports, the global AI market is projected to reach staggering heights, with estimates surpassing hundreds of billions of dollars by the decade’s end. Several factors are driving this surge in demand: Data Deluge: With the proliferation of digital platforms and connected devices, the volume of data generated is growing at an unprecedented rate. AI and ML algorithms thrive on data, making them essential for extracting meaningful insights and patterns from this vast sea of information. Competitive Edge: Companies constantly seek ways to differentiate themselves in today’s hyper-competitive business landscape. AI and ML offer a significant competitive advantage by enabling organizations to streamline processes, enhance decision-making, and deliver personalized experiences to customers. Cost Efficiency: By automating repetitive tasks and optimizing resource allocation, AI and ML solutions help businesses operate more efficiently, reducing operational costs and maximizing profitability. Innovation Catalyst: AI and ML have the potential to drive transformative innovation across various sectors, from healthcare and transportation to retail and agriculture. By pushing the boundaries of what’s possible, these technologies pave the way for groundbreaking discoveries and advancements. Why Choose RheoData for AI Projects? Amidst the growing demand for AI and ML solutions, selecting the right partner to spearhead your projects is paramount to success. Here’s why RheoData stands out as the ideal choice: Expertise and Experience: RheoData boasts a team of seasoned professionals with deep expertise in Data Integration, Machine Learning (ML), Artificial Intelligence (AI), and Data Science. With years of hands-on experience across diverse industries, our experts have the knowledge and skills to effectively tackle your complex AI challenges. Customized Solutions: At RheoData, we understand that every business is unique, with its goals, challenges, and opportunities. That’s why we take a tailored approach to AI project development, crafting bespoke solutions that align with your specific requirements and objectives. Cutting-edge Technologies: Keeping pace with the latest advancements in AI and ML is crucial for delivering innovative solutions that drive tangible results. RheoData leverages cutting-edge technologies and best practices to ensure our clients stay ahead of the curve and capitalize on emerging opportunities. End-to-End Support: From initial concept to deployment and beyond, RheoData provides comprehensive support at every stage of the AI project lifecycle. Whether you need assistance with data collection, model training, or performance monitoring, our dedicated team guides you every step of the way. Focus on Value Delivery: RheoData aims to deliver measurable value to our clients through AI and ML solutions. We prioritize outcomes over outputs, constantly striving to exceed expectations and drive tangible business impact. Conclusion As the demand for Artificial Intelligence (AI) and Machine Learning (ML) continues to soar, organizations must partner with trusted experts to unlock the full potential of these transformative technologies. RheoData stands at the forefront of AI innovation, offering unparalleled expertise, customized solutions, and unwavering support to help businesses thrive in the digital age. By harnessing the power of AI with RheoData, organizations can embark on a journey of discovery, innovation, and success. In a world where data is king, RheoData empowers businesses to reign supreme with AI-driven insights and solutions. Together, let’s embrace the future of technology and pave the way for a smarter, more efficient tomorrow.", "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" : "Harnessing the Power of AI and Machine Learning with RheoData: A Path to Success", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/HarnessingAIwithRD-2-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/harnessing-ai", "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" : "The partnership between Oracle and Google Cloud represents one of the most significant collaborative achievements in enterprise technology. Oracle Database@Google Cloud Platform brings together Oracle’s proven database excellence with Google Cloud’s industry-leading infrastructure and AI capabilities, creating unprecedented opportunities for enterprise digital transformation. This strategic alliance enables organizations to leverage Oracle’s advanced database technologies – including Oracle Database 23ai with its revolutionary AI features – while gaining full access to Google Cloud’s comprehensive service portfolio including BigQuery, Vertex AI, and Kubernetes Engine. The result is a unified platform that eliminates the traditional trade-offs between database capability and cloud innovation. As a someone who has led multiple enterprise Oracle migrations and working closely with Google Cloud’s enterprise sales team, we’ve witnessed this partnership deliver exceptional results for organizations seeking to modernize their data infrastructure. While Google Cloud SQL platforms offer solid managed database services for many use cases, Oracle@GCP provides enterprise-grade capabilities that drive competitive advantage and long-term strategic value. Two Paths, One Clear Winner We recently collaborated on evaluating two distinct migration architectures for a client modernizing their Oracle infrastructure on Google Cloud Platform. The contrast illuminated fundamental differences that every IT leader should understand. Path 1: Migration to Google Cloud SQL – A Viable Alternative Google Cloud SQL provides excellent managed database services with PostgreSQL, MySQL, and SQL Server options. These platforms offer strong operational benefits including automated backups, security patching, and high availability configurations. For organizations with simpler database requirements or those looking to standardize on open-source technologies, Cloud SQL represents a solid foundation for cloud operations. However, organizations with complex Oracle workloads may find that Cloud SQL requires additional planning for feature compatibility, performance optimization, and application integration considerations. Path 2: Oracle@GCP with Oracle Database 23ai – The Enterprise Excellence Platform Oracle@GCP deploys Oracle Database through Oracle’s cloud infrastructure services directly within Google Cloud Platform. This architecture provides seamless integration with Google Cloud services while maintaining Oracle’s advanced database capabilities, delivering transformation rather than mere platform conversion. The business case extends beyond technical considerations – it’s about maintaining competitive advantage while achieving cloud benefits. 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 that both technical and sales perspectives must address: 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.Our joint 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 Database 23ai: Purpose-Built for AI-Powered Enterprise Success Oracle Database 23ai delivers over 300 enterprise-grade features designed for competitive differentiation. From our combined technical and sales perspective, these transformational capabilities 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 that Google Cloud SQL platforms cannot match. JSON Relational Duality eliminates the traditional document versus relational trade-off by providing unified access through both SQL and JSON APIs, surpassing the capabilities of standard PostgreSQL or MySQL JSON handling. 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 – functionality unavailable in Google Cloud SQL managed services. Oracle True Cache delivers automatically managed, in-memory SQL and key-value caching that accelerates application performance beyond what Cloud SQL memory configurations can achieve. These aren’t incremental improvements – they’re foundational capabilities that position organizations for the next decade of data-driven competition within Google Cloud’s ecosystem. Why Oracle@GCP Wins the Total Cost Analysis Our comprehensive migration assessment reveals the hidden costs of Oracle-to-Cloud SQL conversion that impact bottom-line results: The 80/20 Reality of Database Migration While basic table structures may convert between platforms, they represent only 15-20% of total migration effort. The remaining 80% includes: PL/SQL to stored procedure conversion (60% of effort) – Complete rewriting for PostgreSQL functions or MySQL procedures Application integration changes (20% of effort) – ORM modifications, connection handling, query syntax adjustments Advanced feature reimplementation (10% of effort) – Partitioning, triggers, and constraints require platform-specific approaches Performance optimization (10% of effort) – Completely different tuning methodologies and capabilities Hidden Considerations for Cloud SQL Migration While Google Cloud SQL migration is certainly achievable, organizations should plan for several implementation aspects: Feature adaptation: Some Oracle-specific functionality may require alternative approaches in PostgreSQL, MySQL, or SQL Server environments Application integration updates: Connection handling, query optimization, and ORM configurations may need adjustment for different database engines Performance tuning methodology: Each Cloud SQL platform has unique optimization approaches that teams will need to master Operational procedures: Database administration practices will require updates for the new platform environments Oracle@GCP Advantage Through Partnership Our clients achieve immediate ROI through preserved investments: Zero application rewrite – Existing PL/SQL code base remains fully functional Retained expertise – Current Oracle DBA skills continue delivering value Maintained performance characteristics – No unknown optimization requirements across multiple platforms Preserved advanced features – Partitioning, advanced analytics, and enterprise security remain intact Strategic Positioning for Long-Term Success Oracle@GCP provides the enterprise foundation your organization needs for sustained competitive advantage within Google Cloud’s ecosystem: Google Cloud Integration Excellence – Oracle’s infrastructure services enable seamless connectivity to BigQuery for analytics, AI Platform for machine learning, Kubernetes Engine for containerization, and Vertex AI for advanced AI/ML workloads while maintaining Oracle’s database excellence. AI-Driven Competitive Advantage – Oracle’s built-in AI capabilities complement Google Cloud’s machine learning and analytics services, positioning organizations at the forefront of data-driven decision making. Performance Scalability – Enterprise-grade database performance that surpasses Cloud SQL limitations, particularly for complex analytical workloads and high-concurrency applications that leverage Google Cloud’s compute infrastructure. Operational Excellence – Unified database management with transparent pricing eliminates the complexity of managing multiple Cloud SQL instances for different workload types. The Partnership Perspective: Technical Leadership Meets Sales Excellence From the RheoData View: Oracle@GCP eliminates the technical risks associated with platform conversion while providing immediate access to Google Cloud’s innovation ecosystem. Your team maintains their Oracle expertise while gaining access to best-in-class cloud services. From the Google Cloud Sales Perspective: Oracle@GCP accelerates customer success on Google Cloud Platform by eliminating migration blockers and reducing project risk. Customers achieve faster time-to-value and higher platform adoption rates when database complexity is removed from the equation. Combined Value: This partnership approach ensures both technical success and business objectives align, creating sustainable competitive advantage through proven technology integration. Our Joint 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 Database 23ai via Oracle@GCP delivers the platform foundation necessary for competitive differentiation over the next 5-10 years. This approach provides true digital transformation – not just database conversion – positioning your organization for sustained success in an AI-powered marketplace while maximizing Google Cloud Platform investments. The Bottom Line for Executive Decision-Making When evaluating Oracle migration options within Google Cloud Platform, consider this strategic question: Does your organization want to invest in a complex multi-platform conversion requiring extensive reengineering, or maintain proven database excellence while gaining cloud benefits? Google Cloud SQL platforms serve specific use cases well, but they cannot match Oracle Database’s enterprise capabilities, advanced analytics features, or AI integration potential. Organizations with significant Oracle investments achieve better ROI by leveraging Oracle@GCP rather than pursuing costly platform conversions. We recommend proceeding with an Oracle@GCP proof of concept to validate performance characteristics and integration capabilities with your specific Google Cloud services. This strategic approach balances innovation with risk management while maintaining business continuity and maximizing both Oracle and GCP investment returns. Contact RheoData at cloud@rheodata.com to discuss how Oracle@GCP can accelerate your organization’s Google Cloud adoption while eliminating the risks and costs associated with heterogeneous database conversion.", "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 GCP Migration: Oracle@GCP vs. Cloud SQL – 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_3gph3a3gph3a3gph-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-gcp-vs-cloud-sql-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" : "Data ingestion is always the beginning stages of getting data into a data warehouse and/or cloud. Recently, we had the opportunity to design, build, and implement a data ingestion solution using Oracle GoldenGate Service (GGS) within Oracle Cloud Infrastructure (OCI). Within the a cloud-to-cloud environment, we were able to ingest 2.9TB of data between 10 to 14 hours compared to a two week proof-of-concept, that was performed by another vendor. This is a huge savings of time and provided our customer, with a Return On Time (ROT) between 95% to 97% – allowing them more time for their DBAs to focus on tasks. In this white paper, you can get a sense of the high-level items that it took to achieve this type of return. Although this was done within a single cloud and between two tenancies, these approaches can be used on-premises, on-premises-to-cloud, and cloud-to-cloud. Where we can help? RheoData are the experts in helping organizations move mission critical database workloads between on-premises resources to the cloud! Wether your organization is considering moving on-premises to on-premises or looking to do a lift-n-shift to the cloud, maintaining operational readiness is the key to a successfully migration! RheoData experts can help you evaluate, plan, and implement a data integration/migration strategy to successfully build for the future! Get in touch today to build your migration strategy! —> sales@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" : "Hyper Data Ingestion with Oracle GoldenGate Service (GGS)", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/HyperDataIngestion1-2-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/hyper-data-ingestion-with-oracle-goldengate-service-ggs", "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" : "Recently we have been talking with customers about their Oracle GoldenGate implementations and how these implementations can benefit their enterprise architecture. During these discussions, one thing that sticks out is the need for performance, performance tuning, and how that actually correlates to what is happening in the database. There seems to be a bit of misunderstanding when it comes to this topic. Let’s try and explain that here! Over the years, we have looked into this topic multiple times and have concluded that Oracle GoldenGate performance is directly tied to the performance of the database which it runs against. At the same time, there are settings that can be adjusted at the Oracle GoldenGate layer to help improve performance based on the data and how that data is to be captured and applied. In giving this some thought, we have come up with a simple yet interesting formula that helps explain how Oracle GoldenGate can help organizations capitalize on their investment of this “plumbing” product. The basis of the formula is Time. Basically, how much data can be captured/applied in the smallest amount of time? In general, Oracle GoldenGate does a “capture” of the data from a source data store and then “applies” this data to a target data store. Depending on the flavor of Oracle GoldenGate the information that is captured is either general information from the redo logs (checkpoint markers) or SQL statements from the transaction logs (older versions of Oracle GoldenGate will also pull the SQL statements). The speed at which this “capture” and “apply” process is done plus the time the database has to wait for access for data will be the determining factoring of how much time it takes to move data. Remember, that Oracle GoldenGate operates on commits. A commit has to happen before data is actually moved. Meaning, the amount of time it takes to commit on both sides has a direct correlation to performance. To illustrate, a basic understanding of how Oracle GoldenGate works is needed. The general flow of how Oracle GoldenGate works is illustrated in the image below: An extract will capture data from either the online redo logs or a transaction log when a commit happens. Then the transaction is retained in a trail file, shipped across the network, and then read and applied on the target. Where the replicat will acknowledge the apply when a commit is processed. To dive a bit deeper and further understand how this is related to performance, a detail review of the Integrated Extract and Intergrated Replicat are below. The Integrated processes are used for the Oracle platform. For heterogenous platforms, similar concepts are used, but not exact. Extract The “capture” process is also know as an Extract. For many Oracle implementations, a single extract is all that is needed to pull high volumes of data. The extract process, within an Oracle context, is pretty efficient. Oracle never releases benchmark numbers and this is why people think they may need more than one extract. The only time you need more than one extract is when you want to break up schemas for business reasons. As already mentioned, a single Integrated Extract will perform well in most use-cases. Starting with Oracle GoldenGate 18c (for Oracle), what is termed as “Classic Extract” has been deprecated and removed as of Oracle GoldenGate 21c. For other versions of Oracle GoldenGate (for non-Oracle, Big Data, and Mainframe), the “Classic Extract” still exists. This means for all Oracle implemenations, the “Integrated Extract” has to be used. By using the “Integrated Extract”, you are directly tieing the capture processes to the Oracle Database and the LogMiner process. Resulting in all transactions being pulled from the online redo logs, unless a checkpoint is found back in the archive logs. In that case, the extract will mine the archive logs and catch up to where it needs to in the redo logs. The Integrated Extract is built to be efficient and ensure transactions are captured in transactional order. The internals of the Integrated Extract can be conceptualized in the below image: The breakdown of the Extract is as follows (left to right): Reader – Reads logfile/redo logs and splits transactions into regions Preparer – Scans regions of logfiles/redo logs and pre-filters based on parameters Builder – Merges prepared records in System Change Number (SCN) order Capture – Formats Logical Change Record (LCR) and passes to GG Extract Once a commit is performed in the Oracle Database, the transactions are read from the online redo logs, prepared, built in transactional order, then actually captured. Once “captured”, the transactions are stored in the trail file. At this point the trail file is shipped across the network to the target location. With the integrated extract being pretty efficient, how can we gage or increase performance of the process? Extract Performance For an extract to perform as expected, the Oracle Database needs to be configured to support the integrated process. This means that items like memory and waits need to be address. The first areas that need to be tuned are memory related: Memory (Oracle related) The memory that the integrated extract requires is tied into the System Global Area (SGA). Each extract that is configured against an Oracle Database requires 1.25G of memory in the Streams Pool (stream_pool_size). This setting is often over looked and customers think they need more than one extract. At the same time, if you need more than one extract, you’ll have to allocate 1.25G of memory per extract. Waits (Oracle related) Waits are a different aspect of Oracle GoldenGate performance. Oracle GoldenGate operates on the premises of commit-to-commit. Meaning, any transaction that is open and not committed will not be replicated. This type of issue is typically seen in developers leaving transactions open and walking away or batch process trying to process large amounts of data without a commit. Both lead to possible wait issues. At the same time, commits can cause issues with waits as well. If a system is configured with control files on different I/O locations (non-ASM), “concurrency” waits can cause huge spikes in performance issues with Oracle GoldenGate. The way to remedy this is to ensure that the application or batch processes are committing frequently. Extract Recommendations For extracts, our recommendations are the following: For each extract, allocate a minimum of 1.5G of memory in the Streams Pool Ensure that applications are committing frequently, based on application needs If using batch processing, increase the frequency of committing Replicat On the other side of the configuration is the replicat. Replicats are used to apply the transactions in a transactional order. Depending on the volume of data, there are different types of replicats that can be used. In total there are five different replicats: Classic Replicat Coordinated Replicat Integrated Replicat Non-Integrated Parallel Replicat Integrated Parallel Replicat Each version of the replicat has it benefits and use-case which it can be leveraged for. In newer implementations of Oracle GoldenGate, it is recommended to use a Parallel Replicat. For practical purposes with Oracle Database, the Integrated Replicat should be used. The other versions of the replicat should be used on a case-by-case determined by the volume of data being processed. The Replicat is actually made up of a few different components. These components are: Replicat (a lightweight streaming api) Inbound Server (multiple components) The Inbound Server is made up of four (4) different pieces that enable the apply process. The pieces are: Receiver – reads the logical change record (LCR) from the trail file Preparer – computes the dependancies between transactions (PK, FK, UK) Coordinator – maintains the order between transactions Apply – applies the transactions in order including conflict, detection, and resolution (CDR) and error handling Replicat Performance Similar to the extract process, the performance of the replicat relies on the performance of the database. This means items like memory and waits can have an affect on the performance. The first area that needs to be addressed for performance tuning is memory. Memory (Oracle related) The memory that an integrated replicat requires is tied to the System Global Area (SGA). With each integrated replicat configured against an Oracle Database, the process requires 1.25G of memory in the Streams Pool (stream_pool_size). This allows the for the transactions to be funneled into the streaming api and cached. Then the receiver process can read the LCRs quickly, enabling the preparer and coordinator to put the transactions in order. Once the transactions are in order, the apply process can apply the transactions to the database. If needed the apply process can be scaled based on the parallelism settings in the database. All this is coordinated and done within the memory allocation defined with the SGA and streams pool. Waits (Oracle related) Similar to the extract process, waits within the Oracle Database can have an impact on the performance of Oracle GoldenGate. The biggest concern that can cause a wait for Oracle GoldenGate is the timing on which commits happen. If a commit is not happening frequently, then the corresponding lag will increase. Similar if a commit is happening so soon, lag will not show any performance issue but you may get concurrency waits due to control file writes. The commit frequency of the the application, by business defitions, will have a direct impact on the performance of both the Oracle Database and Oracle GoldenGate. GoldenGate Parameters There are a few Oracle GoldenGate parameters that can help with either identifying performance related issues or ensure a level of performance within the replication stream. These parameters have been broken down into they processes that they support. Extract Parameters LOGALLSUPCOLS – instructs the extract to write all supplemental log columns to the trail file UPDATERECORDFORMAT – writes a single LCR that contains both the before and after images of the transaction. Setting this to COMPACT will reduce the amount of data in the trail file. PARALLELISM – controls the number of prepares that are used to process online redo logs. MAX_SGA_SIZE – controls the amount of memory configured per extract. Typically set to 1.5G per process Replicat Parameters COMMIT_SERIALIZATION – used to define how transactions are ordered. Default is DEPENDENT_TRANSACTIONS. Set to FULL if needing source commit order. EAGER_SIZE – Threshold to begin applying large transactions (9500 (default)). Serializes apply process. MAX_SGA_SIZE – controls memory resources for Integrated Replicat. Defaults to INFINTE PARALLELISM – controls number of appliers (defaultL 4) MAX_PARALLELISM – controls max number of appliers. Note: MAX_PARALLELISM = PARALLELISM, disables auto tuning of replicat Results By understanding what the Integrated Extract and the Integrated Replcat processes do, we can quickly identify ways to increase performance. Using the basic information outlined, we were able to product a 94% increase in performance on small Dell T110 (i3) machines. This was a remarkable increase in performance per minute. If you are looking for more information on how to turn your Oracle GoldenGate or Oracle Database; contact us at hello@rheodata.com.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Performance with Oracle GoldenGate", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/gg_performance-scaled-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/performance-with-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" : "Oracle GoldenGate doesn’t have a native high availability solution. For a tool that’s mission-critical to your data replication strategy, that’s a problem you can’t ignore. The Real Cost of No Native HA Every CTO and IT Director knows the drill. Your GoldenGate environment goes down, and suddenly you’re scrambling. Your DBAs are building complex workarounds. Your data pipeline stops. Your business loses money—potentially thousands per minute Oracle’s answer? Complex MAA (Maximum Availability Architecture) installations that require additional software, extensive configuration, and weeks of implementation time. That’s not a solution—that’s a project. Enter RheoData ActivePass: HA That Actually Works Here’s what you actually need: **A simple, effective active/passive solution that guarantees continuous change data capture**. That’s exactly what ActivePass delivers. Key Business Benefits: Zero Complexity: No additional software installations. No complex MAA setups. Just straightforward active/passive architecture that works. Rapid Failover: When disaster strikes, ActivePass ensures quick, reliable failover. Your data keeps flowing while your competition is still reading recovery manuals. Cost-Effective: At $750 per server, you’re looking at a fraction of what you’d spend on complex enterprise solutions or downtime losses. Immediate ROI: Deploy in days, not weeks. Start protecting your data pipeline immediately. Why This Matters Now In the age of AI and real-time analytics, your data pipeline isn’t just infrastructure—it’s your competitive advantage. Every minute of downtime is a minute your competitors gain ground. ActivePass transforms Oracle GoldenGate from a single point of failure into a resilient, always-on data highway. No drama. No complexity. Just continuous data flow that keeps your business moving forward. The Bottom Line You have two choices: Keep gambling with GoldenGate’s lack of native HA Implement a proven solution that eliminates the risk Smart CTOs and IT Directors don’t wait for problems to become disasters. Ready to Eliminate Your GoldenGate HA Risk? Let’s cut through the complexity and get your data protected. Two ways to move forward today: See it in action: Contact us at bobby.curtis@rheodata.com for a 30-minute demo Ready to secure your data? Purchase your ActivePass licenses directly Don’t let Oracle’s HA gap become your data disaster. Take action now. Contact RheoData at: hello@rheodata.com", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "The Oracle GoldenGate HA Gap: Your Data Can’t Wait for Oracle to Catch Up", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_76194549-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/goldengate-ha-gap", "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" } } ```