--- title: RheoData Blog | real-time data replication description: real-time data replication | 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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[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 # real-time data replication ## [Why Monolithic Databases Win for AI: GoldenGate & Oracle 26ai](https://rheodata.com/en-us/blog/monolithic-ai-database-goldengate-oracle-26ai) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Dec 16, 2025 8:16:27 PM Let's talk about a challenge I'm seeing across organizations right now: teams are racing to... [CONTINUE READING](https://rheodata.com/en-us/blog/monolithic-ai-database-goldengate-oracle-26ai) ## [Controlling Snowflake Costs: The Apache Iceberg Strategy with Oracle GoldenGate 23ai DAA](https://rheodata.com/en-us/blog/controlling-snowflake-costs-apache-iceberg-oracle-goldengate-23ai) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Dec 1, 2025 11:08:44 AM Let me give you the straight story on a challenge I'm seeing across the enterprise landscape:... 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[vector database consolidation (1)](https://rheodata.com/en-us/blog/tag/vector-database-consolidation) See all ##### 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" : "real-time data replication | RheoData Blog Posts", "headline" : "<span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text" >rheodata_blog listing page</span>", "image" : "", "mainEntityOfPage" : { "@id" : "https://rheodata.com/en-us/blog/tag/real-time-data-replication", "@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" : "Let's talk about a challenge I'm seeing across organizations right now: teams are racing to implement AI solutions, but their data pipelines can't keep pace with what AI models actually need. At RheoData, we've watched companies invest millions in cutting-edge AI capabilities only to have their initiatives stall because their data infrastructure wasn't built for the real-time, high-quality data flows that AI demands. Here's the story—AI is only as good as the data feeding it. Your models need fresh, accurate, consistent data flowing continuously. Miss any of these elements, and you're building on a shaky foundation. This is exactly why we've focused our practice on building AI-ready data frameworks that address these fundamental challenges. The Real Problem with Traditional AI Data Pipelines In our work helping enterprises transform their data infrastructure, we keep seeing the same pain points. Traditional batch processing creates data lag that renders AI insights stale before they're actionable. Data quality issues multiply across systems, creating inconsistencies that AI models struggle to handle. And when you're moving data between on-premises systems and cloud platforms, the complexity compounds quickly. What does this look like in practice? Your fraud detection model is working with yesterday's transaction data. Your customer recommendation engine is making suggestions based on outdated inventory. Your predictive maintenance AI is analyzing equipment data that's hours old when seconds matter. These aren't just technical problems—they're business problems that impact your competitive position. At RheoData, we've built our expertise around solving exactly these challenges, helping organizations create data pipelines that actually deliver on AI's promise. How Oracle GoldenGate Changes the Game Oracle GoldenGate has been a cornerstone of data replication for years, but its value for AI pipelines is often underestimated. Through our implementation work across multiple industries, we've identified what makes it particularly powerful for AI workloads. Real-Time Data Movement GoldenGate captures and replicates data changes as they happen—we're talking milliseconds, not hours. For AI applications, this means your models are working with current data. We've implemented GoldenGate architectures where fraud detection models now analyze transactions in near real-time instead of hours later. Recommendation engines reflect inventory changes immediately. Predictive models are actually predictive instead of reactive. Bidirectional Replication for Hybrid Environments Most organizations aren't living in a single cloud or purely on-premises anymore. At RheoData, we specialize in complex hybrid architectures—we handle bidirectional replication seamlessly, whether you're moving data between on-premises Oracle databases and OCI, integrating with Google Cloud Platform, or maintaining data consistency across multiple clouds. Your AI pipeline doesn't need to care where the data lives—we architect GoldenGate topologies that ensure data flows where it needs to go. Data Transformation in Transit Here's where our expertise becomes particularly valuable for AI workloads. GoldenGate doesn't just move data—we configure it to transform data during replication. Filter out irrelevant records. Mask sensitive information to maintain compliance. Aggregate data for specific AI model requirements. You're not just replicating data; we're preparing it for AI consumption on the fly. Minimal Impact on Source Systems We've seen too many data integration projects bog down production systems. GoldenGate uses a log-based approach that reads transaction logs rather than querying tables directly. In our implementations, operational systems keep running at full speed while AI pipelines get the data they need—a critical balance we maintain in every architecture we design. Oracle Database 26ai: Purpose-Built for AI Workloads Now let's talk about Oracle Database 26ai, which Oracle is positioning specifically for AI and machine learning workloads. At RheoData, we've been working with Oracle's AI-enhanced database capabilities since their early releases, and we're seeing meaningful enhancements that address AI-specific challenges. Vector Search Capabilities Database 26ai includes native vector search functionality. If you're working with large language models, embeddings, or similarity searches—common requirements for modern AI applications—you can now store and query vector data directly in the database. We've architected solutions that eliminate separate vector databases, reducing integration complexity and improving performance. In-Database Machine Learning Oracle Machine Learning is deeply integrated, allowing you to build, train, and deploy models directly where your data lives. We help data science teams work directly with production data (appropriately secured, of course) without complex extract-transform-load processes. The result? Faster model training and simplified architecture. AI-Optimized Performance Database 26ai includes optimizations for AI query patterns. When you're running complex analytical queries to train models or serving predictions at scale, these optimizations translate to faster model training and lower-latency predictions. Our team tunes these configurations to match your specific AI workload patterns. Automated Data Management AI workloads are data-intensive. Database 26ai includes enhanced autonomous capabilities that automatically tune performance, manage storage, and optimize query execution based on AI workload patterns. We layer our governance and monitoring frameworks on top of these capabilities to ensure your database administration teams focus on strategy rather than constant tuning. Bringing It Together: The RheoData AI Framework Approach Here's where GoldenGate and Database 26ai become particularly powerful together, and where RheoData's expertise delivers real value. Let me walk through the framework architecture we've refined across multiple client implementations. Real-Time Data Ingestion We configure GoldenGate to continuously capture changes from your operational databases—Oracle, SQL Server, MySQL, whatever you're running. These changes flow in real-time to Database 26ai, where they're immediately available for AI model consumption. In our implementations, data freshness goes from hours or days to seconds. Hybrid Cloud Flexibility Your operational systems might be on-premises, your data lake in Google Cloud Platform, and your AI training environment in OCI. At RheoData, we're experts in both Oracle and Google Cloud ecosystems—we architect the connectivity and data flow across all of these environments. Database 26ai can sit at any point in this architecture, serving as your AI-optimized data platform wherever it makes sense for your workload. Data Quality and Preparation As we configure GoldenGate to move your data, we implement transformations and quality checks based on your AI model requirements. By the time data reaches Database 26ai, it's clean, properly formatted, and ready for AI consumption. Your data scientists spend time building models instead of cleaning data—exactly what we aim for in every engagement. Unified Data Access We design Database 26ai as a single point of access for your AI applications. Whether you're training models, serving real-time predictions, or running analytical queries, you're working with consistent, current data. No more data silos creating conflicting results. Implementation Considerations: The RheoData Methodology Let's talk about what it takes to make this work in your environment. At RheoData, we've developed a proven methodology for implementing AI-ready data frameworks. Start with Your AI Use Cases We don't build infrastructure in search of a problem. Our engagements start by identifying your AI initiatives that are being constrained by data pipeline limitations. Where would real-time data make a meaningful business difference? Which AI workloads are struggling with data quality or freshness? We help you focus effort where it delivers the most value. Design for Data Governance Real-time data flows are powerful, but they need proper governance. We build in data quality checks, establish clear ownership, and ensure compliance requirements are met as data moves through your pipelines. Our team leverages GoldenGate's transformation capabilities to embed governance into the data flow itself. Architect Your Hybrid Environment We're realistic about where your data lives now and where it needs to be. Most organizations are operating in hybrid environments, and RheoData specializes in these complex architectures. We design GoldenGate topologies that handle your current reality while being flexible enough to adapt as your environment evolves. Implement Comprehensive Monitoring Real-time pipelines need real-time monitoring. We implement comprehensive monitoring for GoldenGate replication lag, Database 26ai performance metrics, and data quality indicators. You can't manage what you can't measure—we ensure you have visibility into every aspect of your AI data pipeline. Build Team Capabilities Your team needs to understand both GoldenGate administration and AI workload optimization in Database 26ai. RheoData provides training and knowledge transfer as part of every implementation. The best architecture fails without teams capable of operating it effectively, and we ensure your teams are equipped for long-term success. The RheoData Advantage What makes RheoData different in this space? We bring deep expertise across the entire Oracle and Google Cloud ecosystems. Our team has implemented complex data replication architectures for decades, and we've evolved that expertise specifically for AI workloads. We understand both the strategic business requirements and the tactical implementation details that make the difference between a proof of concept and a production system that delivers business value. We don't just implement technology—we build frameworks that your teams can operate, maintain, and evolve. Our goal is your team's success, and we measure our success by the AI capabilities you're able to deploy after we've worked together. Moving Forward: Let's Execute on This AI is transforming how organizations operate, but success requires infrastructure that can keep pace with AI demands. Oracle GoldenGate and Database 26ai provide a powerful foundation for building resilient, real-time AI data pipelines that can scale with your ambitions. The question isn't whether you need better data infrastructure for AI—you do. The question is whether you're ready to address it strategically before data pipeline limitations constrain your AI initiatives. At RheoData, we've built our practice around helping organizations like yours implement AI-ready data frameworks that deliver results. What does success look like here? AI models that work with current data. Faster time-to-insight. Simplified architecture that your teams can operate. And the flexibility to adapt as your AI strategy evolves. I'd value your perspective on this. What data pipeline challenges are you seeing with your AI initiatives? Where are traditional approaches falling short? Let's continue this conversation—reach out to our team at RheoData - cloud@rheodata.com - and we'll explore how we can help you build the data infrastructure your AI strategy deserves.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "16/12/2025", "datePublished" : "16/12/2025", "headline" : "Why Monolithic Databases Win for AI: GoldenGate & Oracle 26ai", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Gemini_Generated_Image_zfy4knzfy4knzfy4.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/monolithic-ai-database-goldengate-oracle-26ai", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Let me give you the straight story on a challenge I'm seeing across the enterprise landscape: Snowflake costs are climbing, and many organizations are looking for strategic ways to control their data platform spend without sacrificing capability. The good news? There's a proven approach that puts you back in the driver's seat. The Cost Challenge Snowflake delivers exceptional performance and ease of use—that's not up for debate. But here's what I'm seeing in the field: as data volumes grow and analytics teams scale their workloads, compute costs can become unpredictable. Every query spins up compute resources, and those credits add up fast. For organizations running continuous analytics, real-time dashboards, or heavy transformation workloads, the monthly bill can become a serious line item. What's our objective here? We want to maintain the analytical flexibility that Snowflake provides while controlling where we spend our compute dollars. The solution lies in understanding a fundamental truth about modern data architecture: storage and compute don't have to be coupled. Apache Iceberg: The Strategic Foundation Apache Iceberg is an open table format that's changing how we think about data lake architecture. It brings the reliability and simplicity of SQL tables to cloud object storage while enabling multiple compute engines—Spark, Trino, Flink, Presto, and yes, Snowflake—to work with the same tables simultaneously. Here's where it gets interesting for cost control: Iceberg tables store their data and metadata files in external cloud storage that you manage. When Snowflake connects to these external Iceberg tables, something significant happens from a billing perspective: Snowflake does not charge storage costs for Iceberg tables. Your cloud storage provider bills you directly—and hyperscaler object storage (S3, GCS, Azure Storage) is significantly less expensive than Snowflake-managed storage. But storage savings are just the opening move. The real strategic advantage is in compute. Offloading Compute to the Hyperscaler When you store your data in Apache Iceberg format on hyperscaler object storage, you gain the ability to choose which compute engine runs your queries. Need to run a heavy transformation job? Spin up a Spark cluster on AWS EMR, Google Dataproc, or Azure HDInsight. Running ad-hoc analytics? Fire up Trino or Presto. Want the Snowflake experience for specific workloads? Connect Snowflake to your Iceberg tables using an external volume—you'll only pay for the compute cycles you actually use. This is what I call compute portability, and it's a game-changer for cost management. The hyperscalers offer reserved instances, spot pricing, and committed use discounts that can dramatically reduce your compute costs compared to on-demand Snowflake credits. You're not locked in—you're making strategic choices based on workload requirements and budget constraints. Oracle GoldenGate 23ai DAA: Your Data Pipeline Now let's talk execution. How do you get your data into Apache Iceberg format continuously and reliably? This is where Oracle GoldenGate 23ai Distributed Analytics and Applications (DAA) delivers. GoldenGate's Iceberg Replicat writes directly to Iceberg tables without requiring a SQL engine. It uses the Iceberg Java SDK along with object storage-specific SDKs to write data directly to your hyperscaler storage. This means: Real-time data replication from your source databases to Iceberg tables No intermediate SQL engine consuming compute resources during the write process Direct writes to S3, GCS, or Azure Data Lake Storage in Parquet format Full ACID transaction support with proper handling of inserts, updates, and deletes The Replicat process handles all the complexity of Iceberg's specification—metadata management, snapshot creation, and delete file generation—so your data arrives ready for analytics. The Architecture in Practice Here's how a typical implementation looks: Source Systems → Oracle, SQL Server, PostgreSQL, or other supported databases where your transactional data lives. Oracle GoldenGate 23ai DAA → Captures changes in real-time and replicates to Iceberg format. Supports multiple catalog types including AWS Glue, Polaris, Nessie, and REST catalogs. Hyperscaler Object Storage → Your data lands in S3, GCS, or ADLS in open Parquet format, organized as Iceberg tables. Compute Engines → Choose your weapon based on the mission: Heavy ETL/transformation: Spark on your preferred hyperscaler Interactive analytics: Trino, Presto, or Dremio Specific Snowflake workloads: Connect via external volume for seamless querying Snowflake (Optional) → Query your Iceberg tables through catalog integration. You get Snowflake's query experience without Snowflake storage costs, and you only pay compute when you actually query. The Cost Math Let's look at a practical scenario. Say you have 50TB of analytical data that's refreshed continuously throughout the day. Traditional Snowflake Approach: Storage: Snowflake-managed (premium pricing) Compute: All queries run on Snowflake credits You're paying Snowflake for everything Iceberg + GoldenGate Approach: Storage: Hyperscaler object storage (significantly lower cost per TB) Heavy compute: Spark clusters with spot/reserved pricing Ad-hoc analytics: Trino or Snowflake, depending on need You're paying hyperscaler rates for storage and choosing the most cost-effective compute for each workload Organizations I've worked with have seen 40-60% reductions in their total data platform costs using this approach—and they've gained flexibility they didn't have before. Configuration Essentials For teams ready to execute, here's what you need to know about the GoldenGate configuration. The Iceberg Replicat supports automatic configuration—set gg.target=iceberg and the handler autoconfigures the required components. Key configuration properties: gg.target=iceberg gg.eventhandler.iceberg.catalogType=glue # or nessie, polaris, rest gg.eventhandler.iceberg.fileSystemScheme=s3:// gg.eventhandler.iceberg.awsS3Bucket=your-iceberg-bucket gg.eventhandler.iceberg.awsS3Region=us-east-2 GoldenGate handles automatic table creation, operation aggregation, and proper Iceberg metadata management. The default flush interval is 15 minutes, configurable based on your latency requirements. Snowflake Integration Once your data is in Iceberg format on your hyperscaler storage, Snowflake connects through an external volume and catalog integration. Snowflake supports both scenarios: External catalog (AWS Glue, Polaris, REST): Snowflake reads metadata from your existing catalog Snowflake as catalog: Snowflake manages the Iceberg metadata while data stays in your external storage Either way, your Iceberg tables show up in Snowflake like native tables, but the storage costs stay with your hyperscaler agreement—where you likely have better rates and more control. Mission Accomplished: What Success Looks Like When this architecture is implemented correctly, you achieve several strategic objectives: Cost predictability: Storage costs are transparent and controlled through your hyperscaler agreement Compute flexibility: Choose the right engine for each workload without lock-in Data openness: Your data is in open formats (Parquet, Iceberg), accessible by any compatible tool Real-time currency: GoldenGate keeps your Iceberg tables current with continuous replication Operational simplicity: One data copy serves multiple compute engines Taking Action If you're facing Snowflake cost challenges, here's my recommendation: start with a single high-volume table or workload. Implement GoldenGate 23ai DAA replication to Iceberg on your hyperscaler of choice. Measure the cost differential over 30 days. The numbers will tell the story. This isn't about replacing Snowflake—it's about using the right tool for each part of the mission. Snowflake excels at interactive analytics and business intelligence. Spark excels at heavy transformations. Iceberg lets them coexist on the same data without duplicating storage or sacrificing capability. Outstanding work comes from making strategic architectural decisions. This is one of them. Have questions about implementing this architecture? Let's coordinate. Reach out to discuss how RheoData can help you achieve your data platform objectives.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "01/12/2025", "datePublished" : "01/12/2025", "headline" : "Controlling Snowflake Costs: The Apache Iceberg Strategy with Oracle GoldenGate 23ai DAA", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Snowflake-Cost-Control-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/controlling-snowflake-costs-apache-iceberg-oracle-goldengate-23ai", "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" } } ```