--- title: RheoData Blog (10) description: RheoData Blog Posts (10) --- [![RheoData - Logo - transparent-1](https://rheodata.com/hs-fs/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png?width=250&height=50&name=RheoData%20-%20Logo%20-%20transparent-1.png "RheoData - Logo - transparent-1")](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - [Exadata & Oracle Database](https://rheodata.com/exadata-and-oracle-database-26ai) - Verticals - [Manufacturing](https://rheodata.com/manufacturing) - [Retail](https://rheodata.com/retail) - [State & Local](https://rheodata.com/sled) - [Customer Stories](https://rheodata.com/customer-stories) - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration) - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) [![](https://rheodata.com/hs-fs/hubfs/Imported_Blog_Media/blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png?width=100&height=100&name=blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png)](https://rheodata.com/) [![RheoData - Logo - transparent-1](https://rheodata.com/hs-fs/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png?width=250&height=50&name=RheoData%20-%20Logo%20-%20transparent-1.png "RheoData - Logo - transparent-1")](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - [Exadata & Oracle Database](https://rheodata.com/exadata-and-oracle-database-26ai) - Verticals - [Manufacturing](https://rheodata.com/manufacturing) - [Retail](https://rheodata.com/retail) - [State & Local](https://rheodata.com/sled) - [Customer Stories](https://rheodata.com/customer-stories) - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration) - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) [![](https://rheodata.com/hs-fs/hubfs/Imported_Blog_Media/blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png?width=100&height=100&name=blog-feature-Logo-Nov-26-2025-07-14-39-7226-PM.png)](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - [Exadata & Oracle Database](https://rheodata.com/exadata-and-oracle-database-26ai) - Verticals - [Manufacturing](https://rheodata.com/manufacturing) - [Retail](https://rheodata.com/retail) - [State & Local](https://rheodata.com/sled) - [Customer Stories](https://rheodata.com/customer-stories) - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration) - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) # RheoData Blog ### Browse By: Everything Cloud GoldenGate Business Insights AI 19c ai pipelines 18c 21c 12c machine learning 23ai database goldengate 21c 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the search field is empty. ## [Connecting to MySQL Database Service – SSH and MySQL Shell](https://rheodata.com/en-us/blog/connecting-to-mysql-database-service-ssh-and-mysql-shell) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:38 PM With the Oracle Cloud Infrastructure (OCI) MySQL Database Services, in order to connect you have to... [CONTINUE READING](https://rheodata.com/en-us/blog/connecting-to-mysql-database-service-ssh-and-mysql-shell) ## [Oracle Database Support through 2027 .. Where do you need to be and who can help!?](https://rheodata.com/en-us/blog/oracle-database-support-through-2027-where-do-you-need-to-be-and-who-can-help) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:38 PM About every two to three years, Oracle releases an innovation release of the Oracle Database. The... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-database-support-through-2027-where-do-you-need-to-be-and-who-can-help) ## [Data Pipelines – What is a data pipeline?](https://rheodata.com/en-us/blog/data-pipelines) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:37 PM A data pipeline is a method in which raw data or unchanged data is ingested from various data... [CONTINUE READING](https://rheodata.com/en-us/blog/data-pipelines) ## [Oracle GoldenGate 23c “Free” – What you need to know!](https://rheodata.com/en-us/blog/oracle-goldengate-23c-free-what-you-need-to-know) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:36 PM [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-goldengate-23c-free-what-you-need-to-know) ## [Unlocking the Power of AI Vector Search in Oracle Database 23ai](https://rheodata.com/en-us/blog/23ai-ai-vector-search) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:35 PM Oracle has recently, May 2, 2024, unveiled Oracle Database 23ai, packed with innovative AI... [CONTINUE READING](https://rheodata.com/en-us/blog/23ai-ai-vector-search) ## [Interesting change in ALTER EXTRACT command](https://rheodata.com/en-us/blog/alter-extract-command) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:34 PM Today, while helping a customer, we had to rebuild an extract. The integrated extract that we... [CONTINUE READING](https://rheodata.com/en-us/blog/alter-extract-command) ## [Oracle Autonomous Data Warehouse (ADW)–What and Why?](https://rheodata.com/en-us/blog/oracle-autonomous-data-warehouse-adw-what-and-why) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:34 PM Oracle’s Cloud Infrastructure (OCI) introduced the world to Autonomous Databases and a more... [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-autonomous-data-warehouse-adw-what-and-why) ## [Oracle Cloud World 2022 and what RheoData thinks you need to see.](https://rheodata.com/en-us/blog/oracle-cloud-world-2022-and-what-rheodata-thinks-you-need-to-see) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:33 PM [CONTINUE READING](https://rheodata.com/en-us/blog/oracle-cloud-world-2022-and-what-rheodata-thinks-you-need-to-see) ## [AI Gets a Real-Time Boost: GoldenGate Powers RAG with Fresh Vectors](https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:32 PM Artificial intelligence (AI) is transforming how businesses operate, and one of the most exciting... [CONTINUE READING](https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag) ## [Why GitHub + Local Backup Saves Careers: The Oracle GoldenGate Reality Check](https://rheodata.com/en-us/blog/goldengate-parameter-backup-strategy) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:31 PM I’ve seen three DBAs lose their jobs over Oracle GoldenGate configuration losses in the past two... [CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-parameter-backup-strategy) ### Recent Posts #### [Coordinated Replicats: Faster, Lower-Risk GoldenGate Loads](https://rheodata.com/en-us/blog/coordinated-replicats-initial-load) Posted at Jun 26, 2026 11:08:13 AM ![Post Featured Image](https://rheodata.com/hubfs/Gemini_Generated_Image_509fjc509fjc509f.png) #### [Forward Deployed Engineering: The Operating Model the Agentic Era Demands](https://rheodata.com/en-us/blog/forward-deployed-engineering-agentic-era) Posted at May 30, 2026 11:46:34 AM ![Post Featured Image](https://rheodata.com/hubfs/Gemini_Generated_Image_r8vpntr8vpntr8vp.png) #### [The Bowl Is Broken: A CEO's Note on Mental Health Month and Tech Team Burnout](https://rheodata.com/en-us/blog/tech-team-burnout-mental-health-month-2026) Posted at May 12, 2026 7:23:33 AM ![Post Featured Image](https://rheodata.com/hubfs/IMG_2142.jpg) ### Posts by Tag - [Cloud (88)](https://rheodata.com/en-us/blog/tag/cloud) - [GoldenGate (73)](https://rheodata.com/en-us/blog/tag/goldengate) - 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All Rights Reserved. ```json { "@context" : "https://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "US", "addressRegion" : "GA" }, "description" : "Elite specialist partner for Oracle, Snowflake, BigQuery, and AI-ready data platforms.", "email" : "hello@rheodata.com", "foundingDate" : "2020", "logo" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png", "name" : "RheoData", "sameAs" : [ "https://www.linkedin.com/company/rheodata" ], "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "WebSite", "name" : "RheoData", "potentialAction" : { "@type" : "SearchAction", "query-input" : "required name=search_term_string", "target" : "https://rheodata.com/search?q={search_term_string}" }, "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "", "url" : "" }, "dateModified" : "2026-04-17T17:31:04+0000", "datePublished" : "2025-10-30T17:41:53+0000", "description" : "RheoData Blog Posts (10)", "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/page/10", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData" } } ``` ```json { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Connecting to MySQL Database Service – SSH and MySQL Shell", "mainEntityOfPage": "https://rheodata.com/en-us/blog/connecting-to-mysql-database-service-ssh-and-mysql-shell", "datePublished": "10/11/2025", "dateModified": "10/11/2025", "articleBody": "With the Oracle Cloud Infrastructure (OCI) MySQL Database Services, in order to connect you have to access MySQL through a compute node. This means you will have to create a compute node within OCI. You can do this quickly with Terraform (here). Once you have an OCI compute node built, you will need to install MySQL Shell. MySQL Shell is the advanced MySQL Client for Developers and DBAs. Making interaction with MySQL easier from the command line. Installing MySQL Shell Access the OCI compute node (needs public IP address of compute node) > ssh -I .ssh/mysql_private_key.pem opc@ Install MySQL Shell via command line > sudo yum install -y mysql-shell After MySQL Shell is installed, you can access your MySQL Database Service (database) via the command line. Connecting to MySQL To start MySQL Shell and connect to the DB System endpoint, the following command is used. Keep in mind, you are SSHed into the compute node, from the compute node you’ll use the DB System private IP address to connect. > mysqlsh bcurtis@10.0.0.37 Provide the password for the user that was established when creating the MySQL Database Service. After providing the password, you will be connected to the MySQL Database Service. By default the MySQL Shell will connect you to the JavaScript command line option. I’ve switched to the SQL command line option by using “\sql”. At this point, you can now interact with your MySQL database on Oracle Cloud Infrastructure (OCI).", "image": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/mysql-shell-install-2.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Bobby Curtis", "url": "https://rheodata.com/en-us/blog/author/bobby-curtis", "sameAs": "" }, "publisher": { "@type": "Organization", "name": "RheoData", "url": "rheodata.com", "logo": { "@type": "ImageObject", "url": "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" } } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "About every two to three years, Oracle releases an innovation release of the Oracle Database. The innovation release are geared to give customers new features and enhancements that will be introduced into the next long term release. At the same time, these releases allow Oracle to set a road map of features that will drive the industry forward. The last major long term release was Oracle 11g (11.2.0.4), which has enabled businesses to run enterprise ready workloads for nearly fourteen years. Over this time, Oracle has released three innovation releases and one long term release(12.1, 12.2, 18.1, and 19.1) – with the long term release being Oracle 19.1. The long term release of Oracle 19c (19.1), there are a few dates that need to be kept in mind: Premier Support (PS) on April 30, 2024 + one year free support between May 1, 2024 through April 30, 2025 Error Correction Support (ECS) will be availiable through April 30, 2027 with paid support The image below shows what these release and support timelines look like currently: As previously mentioned, prior to 19c, the last long term release of Oracle 11g (11.2.0.4). This version of the Oracle database has served many organizations for over a decade leading Oracle to change their support strategy in 2021. This changed allowed organizations to continue running 11.2.0.4 until they could define plans to migrate to the next long term release (Oracle Database 19c). This “market driven support” extended the 11g (11.2.0.4) database until this year (2023). With the arrival of 2023, many organizations are finding themselves having to pivot quickly to upgrade to Oracle Database 19c (19.1.x). Many are doing this for two reasons: Financial Relief – lower support costs Migrate to a supported platform before evaluating where to go next Although these are two primary reasons for organizations to move, there is a third option that many tend to ignore. This would be the cloud strategy! With either of these options, it takes time to migrate between database versions and the migrations are not for the faint of heart… after all your business is running on these critical platforms. Where can we help? RheoData is the experts in helping organizations move mission critical database workloads between versions of the Oracle 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" : "Oracle Database Support through 2027 .. Where do you need to be and who can help!?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/oracle_database_lifecycle_support-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-database-support-through-2027-where-do-you-need-to-be-and-who-can-help", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "A data pipeline is a method in which raw data or unchanged data is ingested from various data sources and shipped to another data store, like a relational database, data lake, or a data warehouse; where the data is eventually used for analysis. Before data is eventually used it undergoes some form of data processing or transformation; including filtering, masking, and aggregations. The transformation process ensures data integration and standardization. This is particularly important when the destination for the raw data is a relational database. As the names suggests, data pipelines act as the “piping” or “plumbing” for many different projects in modern data platforms, data science projects, or business intelligence dashboards. Data is can be and often sourced through a wide variety of places – APIs, SQL, NoSQL, flat files, etc., but the data is not ready for immediate use. Preparation of data usually falls on the shoulders of data engineers or data scientist, who structure the data to meet the need of the business and the associated use cases. The type of data processing that a data pipeline requires is usually determined through a mix of exploratory data analysis and defined business requirements. Once the data has been appropriately filtered, merged, and summarized, it can then be stored and used. Well-organized data pipelines provide the foundation for a range of data projects; this can include exploratory data analyses, data visualizations, and machine learning tasks. Types of Pipelines There are a few different types of data pipelines, but two primary types stand out; which are batch processing and stream processing. Batch Processing The development of batch processing was critical step in building data infrastructures that were reliable and scalable in the early days. This type of processing enabled organizations to move and process large amounts of data into repositories at set time intervals, typically during off-peak hours. This way workloads were not impacted as batch processing jobs tend to work with large volumes of data; taxing the overall system. Batch processing is the optimal data pipleline when there isn’t a immediate need to analyze a specific dataset and is more associated with the Extract, Transform, and Load (ETL) data ingestion process. Streaming Data Streaming data is leveraged when it is required for data to be continuously updated. For example, apps or point of sale (POS) systems need real-time data to update inventory and sales history of their products; that way, sellers can inform consumers if a product is in stock or not. A single action, like a product sale, is considered an “event”, and related events, such as adding an item to checkout, are typically grouped together as a “topic” or “stream.” These events are then transported via messaging systems or message brokers, such as the open-source offering, Apache Kafka. Since data events are processed shortly after occurring, streaming processing systems have lower latency than batch systems, but aren’t considered as reliable as batch processing systems as messages can be unintentionally dropped or spend a long time in queue. Message brokers help to address this concern through acknowledgements, where a consumer confirms processing of the message to the broker to remove it from the queue. Architecture of Data Pipelines There are three phases that make of a data pipeline. Data Ingestion Data Transformation Data Storage Within these three phases, data is moved and transformed as needed to ensure data can be used by an organization. Data Ingestion: Data is collected from various data sources, including various data structures (i.e. structured and unstructured data). Businesses can choose to extract data only when they are ready to process it; however, it is best practice to land raw data with a cloud provider first (data warehouse or data lake). This way, business can update historical data if they need to make adjustments to data processing routines. Data Transformation: A series of jobs are executed to process data and transform the data into a format that is required by the destination data repository. Transformation jobs embed automation and governance into the process flow, ensuring that the data is cleaned and transformed accordingly. Data Storage: After data is transformed, the data is then stored within a data repository (commonly, a relational database), where it can be exposed to business stakeholders. Data Pipelines vs ETL Pipelines In many circles, the terms of “data pipeline” and “ETL pipeline” are often interchangeable within a conversation; however, the term “ETL pipeline” should be considered a sub-category of the conversation. Between these two terms of a pipeline, there are distinguished points that need to be understood. ETL Pipelines: follow a specific sequence. As ETL implies, the pipeline extracts data, transform data, and then loads the data into a data repository. Not all data pipelines follow this sequence of events. In fact, changing the order of the processes with an ETL pipeline enables an ELT (Extract, Load, Transform) pipeline. ELT pipelines have be come popular with cloud-native approaches since they do the transformation later in the process. ETL pipelines also tend to imply the use of “batch processing”, but as noted earlier can also be inclusive of stream processing. Data Pipelines: It is unlikely to see a true data pipeline undergo data transformations, like an ETL pipeline. Data pipelines tend to be more focused on feeding data to the end target platform (relational database, data lake, or data warehouse), where additional processes will be used to do the data transformation. Data Pipeline Use Cases With the term “big data” being coined in the 1990’s, the growth of data has continued to grow and projected to reach 180 zettabytes by 2025 (2 years from now). As this growth of data continues, data management and data cleaning becomes an ever-increasing priority and putting more pressure on the use of “data pipelines”. While data pipelines can serve many different functions, the following, broad applications of them within business are mostly seen: Exploratory Data Analysis (EDA): EDA is used by data scientists to analyze and investigate data sets and summarized the sets main characteristics, often employing data visualization methods – making easier for data scientists to discover patterns, spot anomalies, test hypothesis, or check assumptions. Data Visualizations: Representations of data via common graphics (charts, plots, info graphs, etc.). Data visualizations display information and communication of complex data relations and data-driven insights in a way that is easy to understand. Machine Learning (ML/AI): Machine Learning is a sub-branch of Artificial Intelligence (AI) and compute science which focuses on using data and models to imitate the way a human learns, thinks, and gradually improves it accuracy. Through the usage of statistical models, models are trained to make classifications or predictions, uncover key insights within an organization’s data. RheoData Recommendations In the above discussion on Data Pipelines, there is a lot for organizations to think about and how these pipelines may or may not be in place with your organization. Organizations are often looking at the bigger picture or the goal, but not how to transform their existing “pipelines” into more modern approaches of getting data where it is needed. For these reasons, RheoData recommends using the following Oracle products to establish or refresh “data pipelines” and building analytical or machine learning/artificial intelligence (ML/AI) processes today. Oracle GoldenGate / Oracle GoldenGate Service Oracle GoldenGate Steram Processing Oracle Autonomous Data Warehouse / Oracle Autonomous Transaction Processing MySQL Heatwave These products from Oracle can help organizations build robust data pipeline, scalable data lake or data warehouse platforms and ensure timely data processing. Data Pipelines and RheoData RheoData has helped many customers, private and public sectors, gain understanding of their data pipelines and how various Oracle products can be used to enable organizational transformation. Below are a few examples: Shoe Carnival improves data pipeline by upgrading Oracle GoldenGate (here) Altec uses a hyper-volume data pipeline to ingest to Oracle Autonomous Data Warehouse (ADW)(here) American Tire Distributor using Oracle GoldenGate for Big Data to populate Google Cloud Storage (here) Zero-ETL – What is it? (here) Contact Info Give us a call today to schedule a review or build your data pipelines!", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Data Pipelines – What is a data pipeline?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/datapipelines-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/data-pipelines", "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" : "Last week at Oracle Cloud World, the Oracle GoldenGate Product team announced the latest version of Oracle GoldenGate “Free”! This release of Oracle GoldenGate was produced to enable developers and small (under 20GB) production environments to build Oracle GoldenGate in an automated fashion – actually extending replication by building pipelines through a guided and automated process. This is nothing new other than the Product Management team has finally done something that was pushed for in 2018, a year after the Microservices architecture was released (in 2017). That was, build an end-to-end approach of setting up Oracle GoldenGate; enabling easier usage for the end-user! Sadly, from our understanding – the pipeline approach will not make it into the paid release of Oracle GoldenGate 23c and only in the “Free” release. What can Oracle GoldenGate “Free” be used for? This is a great question and was asked multiple times while at Oracle Cloud World last week. You can use Oracle GoldenGate “Free” in the following scenarios: As a development or test environment As a production environment – if your databases is 20GB or smaller As a training tool in a learning environment Note: Oracle GoldenGate “Free” is not supported by Oracle Support. For questions regarding Oracle GoldenGate “Free”, can be posted in the GoldenGate “Free” Community Forums. Note-1: RheoData offers support for Oracle GoldenGate “Free” to customers through our Tactical Assistance Program (TAP). Contact us today to get started (hello@rheodata.com) Limitations to Oracle GoldenGate “Free” Although Oracle GoldenGate 23c “Free” is free, there are limitations that needs to be reviewed and adhered to. These limitations are: Oracle-to-Oracle Only No more than 20GB in size For Container Databases (CDB), this includes the total size of all Pluggable Databases (PDBs) Oracle Database 23c Free is supported, with the exception of Parallel Replicats Support through community forums only*** Patches provided at Oracle’s descretion in the form of new builds in Oracle Container Registery Interaction with other GoldenGate “Free” instances only Oracle GoldenGate “Free” cannot be used with fully licensed Oracle GoldenGate products or other third-party integration tools. Integrated and Non-Integrated Replicats – ensure that you do not use Replicat-only features with licensed Extracts No Active Data Guard or XStream entitlements No Downstream capture support No support for Graph data types ***Refer to Note-1 for support information Note-2: Refrain from changing the Replicat type in the Oracle GoldenGate Microservices console, or making changes to the underlying parameter file. This will affect GoldenGate “Free”’s ability to manage the pipeline. This new option for Oracle GoldenGate is great for Small-to-Medium (SMB) who need a replication solution without breaking the budget. At the same time, enterprise customers can take advantage of this platform to enable developers in small scale development environments. Stay tuned, we will be providing more details on how this “Free” version of Oracle GoldenGate works. For support of Oracle GoldenGate 23c “Free”, contact RheoData at hello@rheodata.com for more information!", "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 23c “Free” – What you need to know!", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/rheodata_GG-Free-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-goldengate-23c-free-what-you-need-to-know", "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 has recently, May 2, 2024, unveiled Oracle Database 23ai, packed with innovative AI capabilities that are transforming the way businesses manage and analyze their data. Among these cutting-edge features is AI Vector Search, a game-changer for organizations seeking to unlock valuable insights from complex vector data. Understanding Vector Data and its Challenges Vector data, also known as feature vectors, is a representation of data in a high-dimensional space. Each data point is described by a set of numerical features, forming a vector. This type of data is commonly used in machine learning and artificial intelligence applications, such as image recognition, natural language processing, and recommendation systems. One of the main challenges with vector data is the difficulty in performing similarity searches. Traditional database systems excel at querying structured data using exact matches or range queries, but they fall short when it comes to finding similar vectors based on their proximity in high-dimensional space. This is where Oracle’s AI Vector Search steps in. Revolutionizing Vector Data Search with AI AI Vector Search in Oracle Database 23ai introduces a paradigm shift in the way vector data is queried and analyzed. Here’s how it works: 1. Indexing Vector Data Oracle Database 23ai allows users to index vector data, treating it as a first-class data type. This means that vector data can be efficiently stored, indexed, and queried just like any other data type in the database. 2. Similarity Searches Oracle AI Vector Search enables users to perform similarity searches on vector data. Instead of looking for exact matches, this feature allows you to find vectors that are similar to a given query vector. It measures the proximity of vectors in high-dimensional space and returns the most similar results. 3. Fast and Scalable Searches The power of Oracle AI Vector Search lies in its ability to perform these searches at lightning speed, even in large and high-dimensional vector spaces. Oracle has optimized the indexing and search algorithms to handle massive datasets efficiently, ensuring that similarity searches are fast and scalable. 4. Integration with Machine Learning Oracle AI Vector Search is seamlessly integrated with Oracle’s in-database machine learning capabilities. This means that users can combine vector data with other types of data, apply machine learning algorithms, and build end-to-end AI applications directly within the database. Use Cases Oracle AI Vector Search opens up a world of possibilities for organizations across various industries: 1. Image and Video Search With AI Vector Search, you can perform content-based image and video searches. For example, a media company can enable users to search for similar images or videos based on visual content, even if they don’t have specific keywords or metadata. 2. Recommendation Systems Vector data is commonly used in recommendation systems to model user preferences. Oracle AI Vector Search can be leveraged to find similar users or items, enabling more accurate and personalized recommendations. 3. Natural Language Processing (NLP) In natural language processing (NLP), vector representations of words and documents (word embeddings) can be used for semantic searches. AI Vector Search allows for finding documents similar in meaning to a given query, even if they don’t share the exact keywords. Conclusion Oracle Database 23ai’s AI Vector Search capability is a breakthrough for organizations seeking to harness the power of vector data. By providing efficient and scalable similarity searches, Oracle is revolutionizing the way businesses analyze and derive insights from complex data. With this feature, organizations can unlock the full potential of their AI and machine learning initiatives, driving innovation and gaining a competitive edge. To explore more about Oracle Database 23ai and its AI capabilities, refer to the official Oracle blog: Oracle 23ai AI: Now Generally Available or contact RheoData @ 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" : "Unlocking the Power of AI Vector Search in Oracle Database 23ai", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/oracle-database-23ai-ai-vector-search-1-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/23ai-ai-vector-search", "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" : "Today, while helping a customer, we had to rebuild an extract. The integrated extract that we rebuilt was stuck in a loop and displaying it was an initial load extract from the HTML5 page (AdminService). The adminclient (cmd line), said it was working fine. After the customer rebooted the their GoldenGate Service (GGS) environment, the integrated extract was still having problems. At this point, we executed an INFO EXTRACT , DETAIL and retrieved the sequence number (EXTSEQ) and the relative byte address (EXTRBA). With this information in hand, we removed the extract and the associated parameter file. After all, we were going to rebuilt the extract. The customer the rebuilt the extract using Microsoft VSCode with the RESTful plug-in (makes it really easy and scriptable). With the integrated extract rebuilt, we attempted to ALTER EXTRACT from the admin service, but there is no options (image 1). Image 1: alter extract no seqno/rba option This lead us to look at altering the extract from AdminClient within GGS. The alter extract command that we use was: adminclient> alter extract , extseq , extrba This command caused a syntax error. For anyone and myself, doing GoldenGate for better part of 15 plus years, this was odd. So, we went and looked up the documentation on ALTER EXTRACT in 19c (here). The command syntax for ALTER EXTRACT for Admin Client is clearly the same as any veteran to Oracle GoldenGate would remember. Admin Client Syntax (19c): ALTER EXTRACT group-name [, BEGIN (NOW | yyyy-mm-dd[ hh:mi:[ss[.cccccc]]]} | EXTSEQNO sequence-number [, EXTRBA archive-offset-number] [, ADD_EXTRACT_attribute] | SCN value] [, DESC [, UPGRADE INTEGRATED TRANLOG] [, DOWNGRADE INTEGRATED TRANLOG [THREADS number]] [, THREAD number] [, ETROLLOVER] [, ENCRYPTIONPROFILE encryption-profile-name ] [CRITICAL [ YES | NO ] [PROFILE profile-name | [AUTOSTART [ YES | NO ] [DELAY delay-number] [AUTORESTART [ YES | NO ]| [RETRIES retries-number ]| [WAITSECONDS wait-number ]| [RESETSECONDS reset-number ]| [DISABLEONFAILURE [ YES | NO ] ] ] ] Then we realized or remembered that we were using GoldenGate Service (GGS) and there might have been a few things different from the 19c release to the 21c release. After all, Oracle GoldenGate Service (GGS) is running on 21c. This prompted me to look at the 21c docs and I was sure it didn’t change; users needed a way to position an extract after rebuilding. The documentation for 21c (here) provide what the syntax is for ALTER EXTRACT in 21c – there are minor differences: Admin Client Syntax (21c): ALTER EXTRACT group-name [, BEGIN {NOW | yyyy-mm-dd[ hh:mi:[ss[.cccccc]]]} | [, EXTRBA archive-offset-number] [, ADD_EXTRACT_attribute] | SCN value] [, DESC [, THREAD number] [, ETROLLOVER] [, ENCRYPTIONPROFILE encryption-profile-name ] [CRITICAL [ YES | NO ] [PROFILE profile-name | [AUTOSTART [ YES | NO ] [DELAY delay-number] [AUTORESTART [ YES | NO ]| [RETRIES retries-number ]| [WAITSECONDS wait-number ]| [RESETSECONDS reset-number ]| [DISABLEONFAILURE [ YES | NO ] ] ] ] [, LOGNUM lognum] [, LOGPOS logpos] What this meant for the customer, is that the extract could be rebuilt; however, to find the correct position to start the extract from we needed to know how to get the correct System Change Number (SCN) or the correct Relative Byte Address (RBA). In discussions with the customer, we decided that it was best to use a known System Change Number (SCN). With the information we had, the customer knew we could go back an hour. From here, we used an old post which I wrote in 2014 on how to convert a timestamp to SCN (here). After the retrieving the SCN, we start the rebuilt extract as follows: adminclient> alter extract , scn The extract started successfully, remained on the correct trail file (we were over 660ish files) and captured data as expected. Lesson learned here was, between versions Oracle likes to change things and we need to keep up. At the same time, we wish subtle differences like this do not get over looked in the release notes.", "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" : "Interesting change in ALTER EXTRACT command", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/oraclegoldengateserviceALTEREXTRACT-2.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/alter-extract-command", "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’s Cloud Infrastructure (OCI) introduced the world to Autonomous Databases and a more automated way of thinking about managing your data. In this first-of-a-kind Autonomous database, Oracle has taken a pioneering leap into the future of database management. Through its pioneering ways, Oracle is bringing the next evolution of database technology to customers. Through its Autonomous Database Services, Oracle is enabling businesses to plug and play database technology without having to personally install, configure, secure, run, and manage. These autonomous services are the keys behind Oracle Cloud Infrastructure (OCI); delivering value propositions that are absent in other cloud providers’ offerings. Oracle Autonomous Date Warehouse (Oracle ADW) delivers: Flexible data ingestion and analysis of a wide variety of data types Security built in with advanced intrusion detection and encryption Reduce cost of administration up to 90% compared to other data warehouses Continuous data integration with standard tool sets Related reading by RheoData: Terraforming your way to Oracle Autonomous Databases What is Oracle Autonomous Data Warehouse? Autonomous Data Warehouse is a fully automated cloud database service optimized for analytic workloads, including data marts, data warehouses, and data lakes. It is preconfigured with columnar format, partitioning, and large joins to simplify and accelerate database provisioning, extracting, loading, and transforming data; running sophisticated reports; generating predictions; and creating machine learning models. With Autonomous Database, data scientists, business analysts, and nonexperts can rapidly, easily, and cost-effectively discover business insights using data of any size and type – deliverable in both the Oracle Cloud Infrastructure or Oracle Cloud@Clustomer behind a customer’s firewall. Key Values of the Autonomous Database Autonomous Management: Eliminate nearly all manual and complex tasks that cost money, take time, and sometimes lead to error. Top Performance: All system aspects are continuously monitored for optimal efficiency with adjustments made in the background depending on workloads, query type, and the number of users. Big Data Enablement: Accelerate analytics and data insight extraction. Instant Elasticity: Preconfigured compute and storage shapes can independently scale up and down, without any downtime. Enterprise-Grade Security: Data encryption by default (both in transit and at rest) and self-upgrades of security patches. Key Use Cases Enterprise Data Warehouse Data Lakes Departmental Data Warehouse Data Science and Machine Learning Data and IoT event streams", "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 Autonomous Data Warehouse (ADW)–What and Why?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/Oracle-Autonomous-Database-2-3.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-autonomous-data-warehouse-adw-what-and-why", "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" : "After a two year hiatus, Oracle is coming back to the conference scene with Oracle Cloud World (OCW). This year the conference will be hosted in Las Vegas, NV from Oct 17-20, 2022 and will cover a lot of topics for Oracle Cloud Infrastructure (OCI) and how you should be leveraging the Gen 2 cloud! Who should be attending Oracle Cloud World this year? GoldenGate Managers and Database Architects – Interested in the latest information on Oracle GoldenGate (Microservices) and where it is going. Data Management Executive Leaders – Interested in modernizing the enterprise data architecture Tech Executive – Interested in positioning for the future growth of the organization With RheoData’s focus on getting data into the cloud and our expertise in Oracle and Automation technologies, we think the following sessions are going to be exciting to attend: GoldenGate: LRN3435 – Move Data to Oracle Autonomous Database using Oracle GoldenGate (RheoData) LRN1501 – Oracle GateGate Roadmap and Strategy LRN3503 – Cloud Database Migrations the Easy Way LRN3533 – OCI GoldenGate with On-Premises and MultiCloud Sources Exadata: LIT4204 – Maximizing Your Oracle Exadata Cloud@Customer Investment LRN3135 – Migrating 200+ PDBs from On-Premises Exadata to ExaCC using ZDM LIT4009 – Lifecycle Management: Terraform/Ansible and Oracle Exadata Database Service LRN3549 – Automate Exadata Database Service with APIs, SDKs, Ansible, and Terraform OCI Automation: LRN4024 – How to Automate Oracle Cloud Infrastructure Deployments with Terraform LRN2180 – 2022 Update: How a Multicloud Strategy Saved the Day (After a Pandemic) We hope to see you there and you can find RheoData resources throughout the conference as well. Drop us a hello@rheodata.com to schedule some time to connect while at Oracle Cloud World!", "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 Cloud World 2022 and what RheoData thinks you need to see.", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/social-og-cloudworld-1200x628-1-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/oracle-cloud-world-2022-and-what-rheodata-thinks-you-need-to-see", "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" : "Artificial intelligence (AI) is transforming how businesses operate, and one of the most exciting areas is Retrieval Augmented Generation (RAG). RAG allows AI models to answer questions and generate content by accessing and referencing external data, making them far more accurate and relevant. But for RAG to truly shine, that external data needs to be up-to-date. That’s where Oracle GoldenGate comes in, especially when dealing with vector databases. What’s the Connection? Vectors and RAG Vectors: AI models often represent data as “vectors” — numerical representations of information. These vectors allow AI to understand the relationships and similarities between different pieces of data. Vector Databases: These specialized databases store and manage vectors, enabling fast and efficient retrieval of relevant information for AI tasks. RAG (Retrieval Augmented Generation): RAG systems use vector databases to find relevant context before generating a response. This allows AI to provide more accurate and contextually rich answers. The Challenge: Keeping Vectors Fresh The problem arises when the underlying data changes. If your vectors are based on outdated information, your AI’s responses will be inaccurate. This is where real-time replication is crucial. How Oracle GoldenGate Solves the Problem Oracle GoldenGate shines by providing real-time data replication, ensuring your vector databases are always synchronized with the source data. Here’s how it benefits AI applications: Real-Time Vector Updates: GoldenGate captures changes in the source data and instantly replicates those changes to your vector database. This ensures that your AI models are working with the most current information. Improved RAG Accuracy: By using fresh vectors, RAG systems can retrieve the most relevant context, leading to more accurate and reliable AI responses. Enhanced AI Performance: Real-time data replication minimizes latency and ensures that AI models have access to the information they need, when they need it. Automation: GoldenGate automates the replication process, reducing the need for manual data updates and minimizing the risk of errors. Diverse Data Support: GoldenGate can replicate data from a wide range of sources, including traditional databases and cloud-based systems, enabling you to integrate data from diverse sources into your vector databases Benefits in Action: Customer Service: Imagine a chatbot that uses RAG to answer customer questions. With GoldenGate, the chatbot can access real-time product information, ensuring accurate and up-to-date responses. Financial Analysis: AI models can use RAG to analyze financial data and identify trends. GoldenGate ensures that the models are working with the latest market data, enabling more accurate predictions. Content Creation: AI can create content based on current events. GoldenGate makes sure the AI has access to the most recent news. In Simple Terms: Oracle GoldenGate acts like a live wire, instantly transferring data changes to your AI’s memory (vector database). This means your AI always has the latest information, resulting in smarter, more accurate, and more helpful responses. By leveraging Oracle GoldenGate, organizations can unlock the full potential of RAG and build AI applications that are truly intelligent and responsive.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "AI Gets a Real-Time Boost: GoldenGate Powers RAG with Fresh Vectors", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/AdobeStock_1124857300-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/ai-gets-real-time-boost-rag", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "I’ve seen three DBAs lose their jobs over Oracle GoldenGate configuration losses in the past two years. Not performance issues. Not design flaws. Simple backup failures. The 3 AM Call That Changes Everything Picture this: Your phone rings at 3 AM. Production replicat REPPROD01 is down. The parameter file is corrupted. Your last backup? That shell script that supposedly ran last week but actually hasn’t worked since someone updated the directory structure two months ago. Your recovery options: Recreate from memory (good luck) Find that email from 6 months ago with “most” of the config Call the vendor for the 4-hour emergency support response By 7 AM, you’re updating your LinkedIn profile. The Secret: GoldenGate Backup Has Been Broken for Years Here’s what Oracle won’t tell you: GoldenGate has never had a proper backup solution. Sure, they gave us: GGSCI/AdminClient commands that dump configs (but where?) Manual copy scripts (that break constantly) “Best practices” documents (that nobody follows) New Configuration Services (limited to database and filesystem) – 23ai The result? 78% of GoldenGate shops have inadequate backup strategies. I know because I’ve audited dozens of them. Why Dual Strategy Isn’t Optional—It’s Survival Let’s talk about what actually saves careers: redundancy with intelligence. Local Backups: Your First Line of Defense Instant recovery when networks fail No dependencies on external systems Compressed .rdbin files that survive infrastructure changes Complete offline operation for air-gapped environments GitHub Integration: Your Version Control Lifeline Every change tracked with who, what, when Diff comparisons to see exactly what changed Branch protection for production configs Pull request workflows for change management Distributed copies across your team The Magic: When Both Work Together Production Issue at 3:47 AM ├── Local .rdbin available: Restore in 30 seconds ├── GitHub history shows: Last change 18 hours ago by jsmith ├── Diff reveals: Only checkpoint frequency modified └── Resolution: Config restored, issue identified, job saved Real-World Career Insurance Case Study: Financial Services Client Their senior DBA accidentally deleted all extract parameter files during a “cleanup.” The damage: 47 extract processes $2.3M per hour in transaction processing Compliance audit in 3 days Without Rewind: 16-hour recovery, $37M in losses, careers ended With Rewind: $ rewind --list —local $ rewind extract EXTPROD01 --recover --restore-path /dirprm # ... 46 more commands # Total recovery time: 4 minutes 32 seconds Stop Hand-Rolling Backup Scripts in 2025 I get it. You’ve got that bash script you’ve been maintaining since 2019. It mostly works. But let me ask you: Does it compress backups to save 60% storage? Does it integrate with your version control? Does it create restore-ready binary packages? Does it log every operation for compliance? Does it work offline when GitHub is down? Does it handle both source and target systems? If you answered “no” to any of these, you’re gambling with your career. CI/CD Integration: Set It and Forget It Here’s how Rewind becomes part of your pipeline, not another tool to manage: Jenkins Pipeline Example stage('Backup GoldenGate Configs') {   steps {       sh ‘’'           # Backup all production extracts           rewind extract EXTPROD01 —yes             rewind extract EXTPROD02 —yes           # Backup all production replicats           rewind replicat REPPROD01 —yes             rewind replicat REPPROD02 —yes           # Verify backups           rewind --list —local       ‘’'   }   post {       success {           echo 'GoldenGate configurations backed up successfully’       }       failure {           mail to: 'dba-team@company.com’,                subject: 'CRITICAL: GoldenGate Backup Failed’,                body: 'Immediate action required’       }   } } GitLab CI Example backup-goldengate: stage: backup script:   - rewind er --yes # Backup all extracts and replicats   - rewind --list -GH # Verify GitHub uploads only:   - schedules # Run hourly artifacts:   paths:     - *.rdbin”     expire_in: 30 days Kubernetes CronJob apiVersion: batch/v1 kind: CronJob metadata: name: goldengate-backup spec: schedule: 0 * * * * # Every hour jobTemplate:   spec:     template:       spec:         containers:         - name: rewind           image: rheodata/rewind:latest           command:           - /bin/sh           - -c             - rewind er --yes && rewind —list The Bottom Line Math Your current “strategy”: Manual backup time: 2 hours/week Script maintenance: 4 hours/month Recovery time when it fails: 4-8 hours Career risk: Catastrophic With Rewind: Automated backup time: 0 hours Maintenance: 0 hours Recovery time: < 5 minutes Career risk: Eliminated Cost: $4,900/year (or $408/month) That’s less than the hourly rate for emergency consulting when your homegrown scripts fail. Three DBAs Who Wish They Had Rewind The Architect who lost 6 months of optimization work when a SAN failure corrupted parameter files. No backups. Started fresh at a new company. The Team Lead whose junior admin ran a “cleanup script” that deleted production configs. Their backup script had been failing silently for weeks. LinkedIn shows “seeking opportunities.” The Consultant who assured the client their hand-rolled backup solution was “enterprise-grade.” Until it wasn’t. Reputation destroyed, contracts cancelled. Your Next Move You have three choices: Keep gambling with scripts and hope your number doesn’t come up Spend weeks building a half-baked solution that you’ll maintain forever Implement Rewind and sleep through the night Here’s my direct advice: Stop pretending parameter file backup isn’t critical. Stop believing your scripts are enough. Stop risking your career on 20-year-old practices. The DBAs who survive the next decade will be those who automated the basics and focused on strategic work. Backup isn’t strategic—it’s survival. Ready to safeguard your career? Start your 30-day trial: hello@rheodata.com Or keep rolling the dice. Your choice. ———————————————————————————————————————————————————————— P.S. – Still think your scripts are enough? Ask yourself: When they fail at 3 AM, who’s getting the call? And more importantly, who’s getting the blame? “oracle goldengate backup script not working” “how to backup goldengate parameter files” “goldengate configuration version control” “automated ogg backup solution” “goldengate backup failed career impact”", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Why GitHub + Local Backup Saves Careers: The Oracle GoldenGate Reality Check", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/rewind-backup-1.jpg", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/goldengate-parameter-backup-strategy", "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" } } ```