---
title: RheoData Blog (11)
description: RheoData Blog Posts (11)
---

[![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/)

<https://rheodata.com/en-us/blog/page/11#minimal-header__mobile-nav__mmenu>

[![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/)

<https://rheodata.com/en-us/blog/page/11#minimal-header__mobile-nav__mmenu>

- 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:

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Exadata on Google Cloud

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<https://rheodata.com/en-us/blog/oracle-goldengate-23ai>

## [Oracle GoldenGate 23ai: Powering Real-Time Data Integration ](https://rheodata.com/en-us/blog/oracle-goldengate-23ai)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:31 PM

Oracle GoldenGate has long been the go-to solution for real-time data integration, and with the...

[CONTINUE READING](https://rheodata.com/en-us/blog/oracle-goldengate-23ai)

<https://rheodata.com/en-us/blog/changing-ssh-keys-on-oci-compute-instances>

## [Changing SSH Keys on OCI Compute Instances](https://rheodata.com/en-us/blog/changing-ssh-keys-on-oci-compute-instances)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:30 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/changing-ssh-keys-on-oci-compute-instances)

<https://rheodata.com/en-us/blog/mysql-8-0-top-4-featues>

## [MySQL 8.0 – Top 4 Features](https://rheodata.com/en-us/blog/mysql-8-0-top-4-featues)

Posted by [rheostage5132](https://rheodata.com/en-us/blog/author/rheostage5132) | Nov 10, 2025 9:29:29 PM

If you are running MySQL 5.7 or earlier and not sure if you should upgrade to MySQL 8.0, we think...

[CONTINUE READING](https://rheodata.com/en-us/blog/mysql-8-0-top-4-featues)

<https://rheodata.com/en-us/blog/what-is-llmops>

## [What is LLMOps?](https://rheodata.com/en-us/blog/what-is-llmops)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:28 PM

## What is LLMOps?

LLMOps, or Large Language Model Operations, refers to the set of practices and...

[CONTINUE READING](https://rheodata.com/en-us/blog/what-is-llmops)

<https://rheodata.com/en-us/blog/rheodata-activepass>

## [Optimize Your Oracle GoldenGate Strategy with RheoData ActivePass](https://rheodata.com/en-us/blog/rheodata-activepass)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:28 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/rheodata-activepass)

<https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate>

## [Using NGINX in unsecure mode for Oracle GoldenGate](https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:27 PM

Sometimes, you just want to run NGINX in unsecure mode (over port 80) for internal environments....

[CONTINUE READING](https://rheodata.com/en-us/blog/using-nginx-in-unsecure-mode-for-oracle-goldengate)

<https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment>

## [Oracle@GCP Setup: Enterprise Database Power Made Simple](https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:26 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/oracle-gcp-setup-simple-enterprise-database-deployment)

<https://rheodata.com/en-us/blog/data-comparisons-and-the-dbms_comparison-package>

## [Data comparisons and the DBMS\_COMPARISON package](https://rheodata.com/en-us/blog/data-comparisons-and-the-dbms_comparison-package)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:25 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/data-comparisons-and-the-dbms_comparison-package)

<https://rheodata.com/en-us/blog/fix-outdated-ai-rag-oracle-google-database>

## [Your AI is Lying to Your Customers. Here’s How to Fix It with RAG.](https://rheodata.com/en-us/blog/fix-outdated-ai-rag-oracle-google-database)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:25 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/fix-outdated-ai-rag-oracle-google-database)

<https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service>

## [Manually purging trail files from OCI GoldenGate Service](https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:24 PM

Oracle GoldenGate Service is Oracle’s cloud offering to quickly use GoldenGate to move data within...

[CONTINUE READING](https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service)

### 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

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- [23ai (9)](https://rheodata.com/en-us/blog/tag/23ai)
- [database (9)](https://rheodata.com/en-us/blog/tag/database)
- [goldengate 21c (9)](https://rheodata.com/en-us/blog/tag/goldengate-21c)
- [artificial intelligence (8)](https://rheodata.com/en-us/blog/tag/artificial-intelligence)
- [data analytics (8)](https://rheodata.com/en-us/blog/tag/data-analytics)
- [Google (7)](https://rheodata.com/en-us/blog/tag/google)
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- [11g (4)](https://rheodata.com/en-us/blog/tag/11g)
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- [12c goldengate (3)](https://rheodata.com/en-us/blog/tag/12c-goldengate)
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- [Database Administration (3)](https://rheodata.com/en-us/blog/tag/database-administration)
- [GoldenGate 23c (3)](https://rheodata.com/en-us/blog/tag/goldengate-23c)
- [MySQL (3)](https://rheodata.com/en-us/blog/tag/mysql)
- [Oracle Cloud Migration (3)](https://rheodata.com/en-us/blog/tag/oracle-cloud-migration)
- [Oracle Database 26ai (3)](https://rheodata.com/en-us/blog/tag/oracle-database-26ai)
- [adminclient (3)](https://rheodata.com/en-us/blog/tag/adminclient)
- [architecture of data pipelines (3)](https://rheodata.com/en-us/blog/tag/architecture-of-data-pipelines)
- [automation (3)](https://rheodata.com/en-us/blog/tag/automation)
- [batch processing (3)](https://rheodata.com/en-us/blog/tag/batch-processing)
- [big data (3)](https://rheodata.com/en-us/blog/tag/big-data)
- [data cleaning (3)](https://rheodata.com/en-us/blog/tag/data-cleaning)
- [data integration (3)](https://rheodata.com/en-us/blog/tag/data-integration)
- [data pipelines (3)](https://rheodata.com/en-us/blog/tag/data-pipelines)
- [data pipelines vs ETL pipelines (3)](https://rheodata.com/en-us/blog/tag/data-pipelines-vs-etl-pipelines)
- [data science (3)](https://rheodata.com/en-us/blog/tag/data-science)
- [data-replication (3)](https://rheodata.com/en-us/blog/tag/data-replication)
- [database-platforms (3)](https://rheodata.com/en-us/blog/tag/database-platforms)
- [enterprise data replication (3)](https://rheodata.com/en-us/blog/tag/enterprise-data-replication)
- [gcp (3)](https://rheodata.com/en-us/blog/tag/gcp)
- [hashicorp vault enterprise (3)](https://rheodata.com/en-us/blog/tag/hashicorp-vault-enterprise)
- [Agentic AI (2)](https://rheodata.com/en-us/blog/tag/agentic-ai)
- [Bring Your Own License (BYOL) Oracle (2)](https://rheodata.com/en-us/blog/tag/bring-your-own-license-byol-oracle)
- [Cloud Database (2)](https://rheodata.com/en-us/blog/tag/cloud-database)
- [Cloud modernization strategy (2)](https://rheodata.com/en-us/blog/tag/cloud-modernization-strategy)
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- [Google Cloud for Oracle workloads (2)](https://rheodata.com/en-us/blog/tag/google-cloud-for-oracle-workloads)
- [Legacy Oracle systems (2)](https://rheodata.com/en-us/blog/tag/legacy-oracle-systems)
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See all

- <https://rheodata.com/en-us/blog/page/10>
- [9](https://rheodata.com/en-us/blog/page/9)
- [10](https://rheodata.com/en-us/blog/page/10)
- [11](https://rheodata.com/en-us/blog/page/11)
- [12](https://rheodata.com/en-us/blog/page/12)
- [13](https://rheodata.com/en-us/blog/page/13)
- <https://rheodata.com/en-us/blog/page/12>

##### About RheoData

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  "articleBody" : "Oracle GoldenGate has long been the go-to solution for real-time data integration, and with the release of Oracle GoldenGate 23ai, it just got even better. Packed with innovative features and enhancements, this latest version takes data replication and synchronization to new heights, empowering organizations to unlock the full potential of their data. What’s New in Oracle GoldenGate 23ai? 1. Enhanced Support for Cloud and Hybrid Environments Oracle GoldenGate 23ai shines when it comes to supporting modern cloud and hybrid architectures. With enhanced cloud capabilities, organizations can seamlessly integrate and replicate data across on-premises, cloud, and hybrid environments. This includes improved support for Oracle Cloud Infrastructure (OCI), making it easier than ever to leverage the power and scalability of the cloud. 2. Real-Time Data Streaming GoldenGate 23ai introduces advanced streaming capabilities, enabling real-time data streaming to popular messaging platforms such as Apache Kafka, Oracle Streaming Service, and Confluent Cloud. This opens up a world of possibilities for event-driven architectures and real-time analytics, allowing organizations to build data pipelines that drive immediate insights and action. 3. Simplified Configuration and Management One of the standout features of GoldenGate 23ai is its simplified configuration and management. The new and improved user interface, Oracle GoldenGate Studio, offers a centralized, intuitive platform for managing all your data replication processes. With a streamlined setup process and enhanced automation, it has never been easier to configure and monitor data replication across diverse environments. 4. Support for Additional Data Sources and Targets GoldenGate continues to expand its support for various data sources and targets, ensuring seamless data integration across heterogeneous systems. New additions include support for Oracle Database 23ai, PostgreSQL, and MySQL, among others. This means organizations can easily replicate data between different database platforms, ensuring data consistency and enabling a wide range of use cases. 5. Enhanced Security and Compliance Data security and compliance are top priorities for any organization, and GoldenGate 23ai delivers enhanced security features to address these concerns. With improved encryption capabilities, organizations can protect data during replication, ensuring that sensitive information remains secure throughout the integration process. 6. Improved Performance and Scalability Performance and scalability have always been hallmarks of GoldenGate, and version 23ai takes this even further. With optimized data processing and improved resource utilization, GoldenGate can handle even the most demanding workloads with ease. This includes enhanced parallel processing capabilities, enabling faster data replication and reduced latency for time-critical applications. 7. Advanced Conflict Detection and Resolution Oracle GoldenGate 23ai introduces advanced conflict detection and resolution capabilities, ensuring data consistency in complex replication scenarios. With improved support for multi-master replication and bi-directional data flows, organizations can confidently manage data updates across multiple systems without the risk of data conflicts or inconsistencies. Real-World Use Cases 1. Real-Time Analytics GoldenGate 23ai enables real-time data streaming to analytics platforms, empowering organizations to gain immediate insights from their data. For example, streaming data to Apache Kafka allows for real-time data processing and analysis, driving faster decision-making and enabling data-driven business strategies. 2. Zero-Downtime Migrations The enhanced cloud support in GoldenGate 23ai makes it the ideal tool for zero-downtime migrations to the cloud. Organizations can seamlessly replicate data from on-premises databases to cloud environments, ensuring continuous data availability and minimizing disruptions during the migration process. 3. Data Distribution and Synchronization With support for multi-master replication and improved conflict resolution, GoldenGate 23ai is perfect for distributing and synchronizing data across multiple sites and systems. This ensures data consistency and enables collaborative work across geographically dispersed teams. Conclusion Oracle GoldenGate 23ai is a testament to Oracle’s commitment to delivering best-in-class data integration solutions. With enhanced cloud support, real-time data streaming capabilities, and simplified management, organizations can unlock new levels of data agility and insights. By choosing GoldenGate 23ai, businesses can power their real-time data integration initiatives and drive digital transformation forward. To learn more about Oracle GoldenGate 23ai and its capabilities, visit the official Oracle blog: Announcing GoldenGate 23ai or contact RheoData @ hello@rheodata.com",
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  "articleBody" : "Every now and then comes the need to update your SSH keys on a server in the cloud. Wither you are doing an general update or changing out your laptop, ssh keys need to be updated. In my case, I was changing laptops from Mac to Windows (not 100% sold on M1/M2 for consulting purposes … story for another time). When I went to interact with other OCI compute nodes that were previously built with OpenSSH key, I noticed I couldn’t get a connection to the compute instances. Leading to the need for changing the SSH keys on these nodes. Now can you change keys if you cannot access the node. Oracle provides some good details on making connections via Instance Console Connections, but I didn’t find this very helpful. Cool yes, helpful not really. This lead me to come up with a bit of a different approach for changing SSH keys – use a new compute instance and change the keys. What we needed to do was the following: 1. Dig out the old laptop and retrieve the OpenSSH keys that were previously used. Just need the private key. Email this to yourself or any other means of getting it over to the Windows machine. 2. Use PuttyGen and import the private key. 3. Once the key has been imported, it needs to be exported to force a new file format. If trying to use an older format, will lead to errors and other issues. 4. One the key is saved, it needs to be uploaded to the new compute node that will handle all the updating of the keys for other compute nodes. 5. Once the old RSA key has been uploaded to the new compute node, making SSH connections to the older nodes should be simple with the following command: $ ssh –i id_rsa2 opc@ The to use can be either the public or private address for the compute node. Since we were already connected to a node within the VCN, we opted for the private address. 6. From here, we needed to add the new authorized key to the ~/.ssh/authorized_keys file. After a few trail and error process, we found that it was easier to copy the private key and “echo” it into the authorized_keys file. $ echo &gt;&gt; authorized_keys Once the authorized_keys have been updated on the compute node, update the Putty configurations for the compute instance and connect to the instance. Now, that all our instances have been updated with the correct keys, the Compute Instance that was provisioned to just change keys can be removed. Oracle provides some great information within their documentation; however, we often find them a little bit difficult to follow. Seems like some of the steps are incomplete or missing something. Leading many to figure out creative ways of getting the same items done. Hopefully, this post will help you or someone else understand how to quickly change the SSH keys on compute nodes. RheoData is a global systems integrator and disabled veteran-owned organization headquartered in Atlanta, Georgia. With operations being established in 2019, RheoData has built its business providing solutions across the enterprise landscape with architecture design/reviews, implementation, data replication, and managed services to ensure high-quality enterprise systems.",
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  "articleBody" : "If you are running MySQL 5.7 or earlier and not sure if you should upgrade to MySQL 8.0, we think the following four features are items you don’t want to miss out on. Features in MySQL 8.0 that are key: 1. Transactional Data Dictionary: MySQL 8.0 is finally given a proper data dictionary! Within the InnoDB, the data dictionary is comprised of internal system tables that keep track of objects (i.e. tables, views, indexes, table columns). Object metadata is directly stored in the InnoDB tablespace. Additionally, for historical reasons the data dictionary tables overlap with information stored in the InnoDB table metadata files (.frm) 2. Roles: The usage of roles in other RDBMS platforms is common. Starting in MySQL 8.0 this is now the preferred method of assigning privileges. Using Roles, privileges can granted like individual privileges, can contain nested roles, and multiple roles can be assigned to a user. Providing a more convenient method of adding, removing, and managing privileges. 3. Common Table Expressions: Also known as WITH queries. Using a WITH query, data can be retrieved as table sets and then expressed as a single table. Enabling greater flexiblity within MySQL query framework. 4. Read-Only Schemas: As they mention, Read-Only Schemas within MySQL allow for simplier database migrations and read-only reporting. To enable a schema for read-only, the option only works with the ALTER SCHEMA command. 5. MySQL Shell Changes [Bonus]: Changes to the MySQL Shell has enabled three new utilities that enable a more streamlined approach of unloading and loading data. Through the JavaScript shell, an instance (util.dumpInstance()) or schema (util.dumpSchemas) can be dumped in parallel threads. Additionally, a data set can be loaded into a MySQL instance with the util.loadDump() utility. Using the new utilities with multi-threaded dump and splitting larger tables, speeds up to 3GB/s for dump and 200MB/s for load have been seen. Lastly, there are compatibility modes for OCI MySQL Heatwave that enables database to cloud migration easiers. With these new an exciting features added to MySQL 8.0, you should consider upgrading today! Contact us to discuss your options for upgrading -&gt; sales@rheodata.com Submit Your Info to Download Slide Deck",
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  "articleBody" : "What is LLMOps? LLMOps, or Large Language Model Operations, refers to the set of practices and tools used to manage, streamline, and operationalize large language models. LLMOps is a cross between LLM and MLOps. LLMs – are a type of foundation model that cna perform a variety of NLP task, including generating and classifying texts, answering questions in a conversational manner and translating texts. MLOps – is a discipline that streamlines and automates the lifecycle of ML models. LLMOps applies MLOps principles and infrastructure to LLMs. Making LLMOps a subset of MLOps. The Need for LLMOps Wtih more and more Large Language Models (LLMs), like Cohere Command, OpenAI GPT-4, and many others, coming to market; they will require more resources and drive more complexity. Due to these reasons, they will require specialized techniques and infrasturcutre for their develoment, deployment, and maintenance. Below are some fo the challenges of operationalizing LLMs: Model Size and Complexity – LLMs are very large and complex models. This makes them difficult to train, fine-tune, and deploy. Data Requirements – LLMs require massive datasets of text and code to train. This can be a challenge to collect and curate. Infrastructure Requirements – LLMs require a lot of computational power and storage. This can be a challenge to provision and manage. Performance – Ensuring LLM performance at scale requires computational resources, time, and highly skilled professionals, which are not always available. Security and Privacy – LLMs can be used to generate sensitive text, such as personal information or creative content. It is important to implement security and privacy measures to protect this data. Interpretability – LLMs are often opaque and difficult to interpret. This can make it challenging to understand how they make decisions and to ensure that they are not biased. Ethical Considerations – LLMs may be subject to bias, toxicity, hallucinations, or other ethical concerns. It is important to implement guardrails to protect against these risks. LLMOps aim to address the challenges associated with managing LLMs and ensure they are efficient and effective for production environments. LLMOps help deploym applications with LLM models securely, efficently, and at scale! What is inclued with LLMOps? Key aspects of LLMOps are: Data Creation, Curation and Management – Organizing, storing, and preprocessing the large amounts of data required for training language models. This includes data versioning, ingestions, and data quality checks. Model Training – Implementing scalable and distributed training processes to train large language models. Includes techniques like parallel processing, distributed computing, and automated hyperparameter tuning. Model Deployment – Deploying large language models into production systems, often as APIs or services. Requires infrastructure setup, load balancing, scaling, and monitoring, to ensure reliable and efficient model serving. Monitoring and Maintenance – Ongoing monitoring of model performance, health, and resource usage. Includes tracking metrics, detecting anomalies and triggering alerts for prompt action. Regular model updates and retraining may also be part of the maintenance process. Security and Governance – Ensuring the security and privacy of large language models and their associated data. This includes access controls, encryption, compliance with regulatory requirements and ethical considerations like Responsible AI. CI/CD – Adopting CI-CD practices to automate the testing, validation, and deployment of LLMs. This enables faster iterations and reduces the risk of errors in production. Collaboration and Reproducibility – LLMOps emphasizes collaboration and reproducibility of LLMs. This includes version control, experiment tracking and documentation to enable collaboration among data scientists, engineers and researchers. Many of these key aspects are similar to MLOps. In LLMOps, they are extended and adjusted to meet the requirments of LLMs. LLMOps Landscape With the ever growing LLM landscape, LLMOps is constantly evolving, new tools and platforms are being developed to meet the needs of organizations. Here are a few: Open Source: Huggnig Face – a leading open-source software company that provides tools and libraries for build and using LLMs. MLRun – an open-source orchestration framework that can be used to operationalize LLMS. Enables scaling and automation of ML and LLM pipelines in a streamlined manner. Vendor Based (sampling): Microsoft – with the Azure platform, Microsoft has provided commercialized access to OpenAI’s LLMs and recently released Azure AI Studio to help develop, scale, and streamline LLMOps. Oracle – On the Oracle Cloud Infrastructure (OCI), has provided access to Cohere’s LLMs through the AI &amp; Automation services. Access to LLMs is done through APIs and development in various languages.",
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  "articleBody" : "In today’s digital age, building data pipelines is a crucial aspect of enterprises across the globe. With businesses increasingly relying on data-driven insights to drive growth and innovation, an efficient, secure, and reliable Oracle GoldenGate strategy is more important than ever. That’s where RheoData ActivePass comes into play. The Power of RheoData ActivePass Designed to enhance Oracle GoldenGate, RheoData ActivePass is an advanced solution that simplifies the complexities of Oracle GoldenGate management and replication. It offers a simplified approach to active-passive architectures, making it the best and only solution of its kind for Oracle GoldenGate. But what sets RheoData ActivePass apart from the traditional MAA solutions? Simplified Active/Passive Architecture First and foremost, RheoData ActivePass eliminates the need for additional software, reducing complexity and streamlining your Oracle GoldenGate environment. This means you can focus on leveraging your data to drive business growth, rather than wrestling with intricate software configurations. Optimized for Oracle GoldenGate RheoData ActivePass is specifically designed to enhance the functionality of Oracle GoldenGate, a powerful and robust data replication and integration software. By integrating seamlessly with Oracle GoldenGate, RheoData ActivePass maximizes data availability, reliability, and efficiency. Cost-Effective Pricing Perhaps one of the most compelling advantages of RheoData ActivePass is its cost-effectiveness. Starting at just $750 per server, RheoData ActivePass provides a high-value solution for businesses of all sizes. Whether you’re a small start-up or a large corporation, RheoData ActivePass delivers unmatched value in terms of functionality and affordability. Why Choose RheoData ActivePass for Oracle GoldenGate? In addition to the advantages outlined above, RheoData ActivePass offers a range of additional benefits that make it a standout choice for businesses looking to optimize their Oracle GoldenGate strategy: Continuous Monitoring: RheoData ActivePass regularly monitors Oracle GoldenGate on the active node to ensure it is up and running. Enhanced Performance: Upon a failover, RheoData ActivePass optimizes the performance of Oracle GoldenGate software, by ensuring the successful startup of the passive node and ensuring your data is always accessible and usable. Summary RheoData ActivePass is a powerful, cost-effective, and user-friendly solution that enhances the functionality of Oracle GoldenGate, simplifying active/passive architectures, and ensuring your business can fully leverage its data assets. Whether you’re looking to improve current Oracle GoldenGate architectures or seeking a simple active/passive strategy for your environment, RheoData ActivePass is the solution you’ve been searching for. With RheoData ActivePass, you can take your Oracle GoldenGate architecture to the next level, driving growth, innovation, and success for your business. Contact us today to learn more about how RheoData ActivePass can transform your data replication strategy. Get your RheoData ActivePass at hello@rheodata.com",
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  "headline" : "Optimize Your Oracle GoldenGate Strategy with RheoData ActivePass",
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  "articleBody" : "Sometimes, you just want to run NGINX in unsecure mode (over port 80) for internal environments. The steps here are similar to what I posted some time ago in this post -&gt; here. I’ll put these steps here as well and highlight (bold) which steps were different. 1. SSH into the RedHat instance $ ssh @ 2. Sudo to Root $ sudo su – 3. Install and confirm installation of Nginx $ dnf -y install nginx &amp;&amp; dnf list install nginx After installing NGINX, the next thing to do is to configure it against Oracle GoldenGate. Configure NGINX 1. Go to the Reverse Proxy directory under $OGG_HOME $ cd $OGG_HOME/lib/utl/reverseproxy 2. Run ReverseProxySettings with options (NO SSL) $ ./ReverseProxySettings -u oggadmin -P —-no-ssl -o ogg.conf http://localhost: 3. Copy the config file to NGINX directory $ sudo cp ogg.conf /etc/nginx/conf.d/nginx.conf 4. Remove or rename the default configuration file $ cd /etc/nginx/conf.d $ mv ./default.conf ./_default.con_ 5. Start NGINX $ sudo nginx &amp; 6. Test NGINX config $ sudo nginx -t 7. Reload NGINX $ sudo nginx -s reload 8. Access ServiceManager and other services without port numbers Open a web browser and navigate to the URL, something similar to this:  http://:80 End Result With NGINX configured, you can now access Oracle GoldenGate on port 80. In the example below, we are using a SecureLink connection and the port numbers are off, but the last two digits are the important ones. They show that we are using Port 80 for the connection.",
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  "articleBody" : "Let me give you the straight story: setting up Oracle Database on Google Cloud Platform used to require weeks of planning, coordination between multiple teams, and enough documentation to fill a small library. Those days are behind us. What you’re about to see is how Oracle@GCP transforms enterprise database deployment into a streamlined process that any database administrator can execute confidently. Why Oracle@GCP Changes Everything Your expertise is invaluable when it comes to database strategy, but you shouldn’t have to spend weeks wrestling with infrastructure complexity. Oracle@GCP delivers the full power of Oracle Database Enterprise Edition with the operational simplicity of Google Cloud’s managed services. Here’s exactly how simple the setup process has become. Step-by-Step Setup: From Search to Success Step 1: Find Oracle Database Services Starting from your Google Cloud Console, simply search for “oracle” in the top search bar. The platform immediately surfaces Oracle Database@Google Cloud as your first option, making discovery effortless and eliminating any guesswork about service availability. Step 2: Choose Your Oracle Solution Google Cloud presents you with clear options for Oracle database deployment. Select “Autonomous Database” to access Oracle’s self-managing database service, which handles routine maintenance tasks automatically while you focus on strategic initiatives. Click “Explore service” under the Autonomous Database option to access the service dashboard. The interface immediately shows you the autonomous database management area where you’ll create and monitor your Oracle instances. Step 3: Navigate to Autonomous Database The Autonomous Database dashboard displays your current instances (if any) and provides a prominent “Create” button for new deployments. This clean interface eliminates complexity while giving you full visibility into your database inventory. Click the “Create” button to launch the database creation wizard. The system guides you through a logical sequence of configuration decisions, ensuring you don’t miss critical settings while maintaining deployment speed. Step 4: Configure Instance Details Enter your Instance ID, Database name, and Display name using your organization’s naming conventions. The system validates your entries in real-time and shows you exactly which fields are permanent versus modifiable later, preventing costly mistakes. Step 5: Select Workload Type Choose from four optimized workload configurations: Data Warehouse, Transaction Processing, JSON, or APEX. Each option is clearly explained with use cases, allowing you to select the configuration that matches your specific performance requirements without extensive research. Step 6: Configure Database Specifications Set your license type (BYOL or new), Oracle Database edition, version, CPU count, and storage requirements. The interface provides clear guidance on scaling options and shows cost implications in real-time, enabling informed decision-making. Step 7: Set Backup Retention Configure your backup retention period from 1-60 days based on your compliance and recovery requirements. Oracle manages the entire backup process automatically, eliminating the operational overhead of traditional backup management. Step 8: Establish Administrator Credentials Create your ADMIN username and secure password for database administration. The system enforces Oracle’s security standards while keeping the credential setup process straightforward and secure. Step 9: Configure Network Access Select your network access model: secure access from everywhere, IP-restricted access, or private endpoint access only. The default secure access option provides immediate connectivity while maintaining enterprise-grade security through database credentials and connection wallets. Step 10: Set Operational Contacts Add notification email addresses for operational updates and announcements. The system keeps you informed of maintenance windows, updates, and any issues without overwhelming your inbox with unnecessary alerts. Step 11: Complete Database Creation Click “Create” to deploy your Oracle Autonomous Database. The system begins provisioning immediately, with typical deployment times measured in minutes rather than hours or days. Real-Time Deployment Monitoring Monitoring Phase 1: Initial Provisioning Your database appears in the dashboard with “Provisioning (0%)” status immediately after creation starts. The real-time status updates keep you informed of deployment progress without requiring constant manual checking. Monitoring Phase 2: Active and Ready Once provisioning completes, your database status changes to “Available” with full resource allocation displayed (2 ECPU, 1 TB storage). The system provides immediate confirmation that your database is ready for connections and workload deployment. Seamless OCI Integration Direct OCI Access Notice the “Manage in OCI” button prominently displayed in your Google Cloud console. This direct integration allows you to leverage Oracle’s native management tools without losing the benefits of Google Cloud’s infrastructure and billing integration. Full OCI Administrative Control Clicking “Manage in OCI” provides immediate access to comprehensive database management within Oracle Cloud Infrastructure. You gain access to advanced configuration options, detailed monitoring, disaster recovery settings, and all enterprise-grade administrative capabilities you expect from Oracle Database. What This Means for Your Organization The setup process you just witnessed typically completes in under 15 minutes from start to finish. Compare that to traditional Oracle database deployments that require infrastructure procurement, OS installation, Oracle software installation, network configuration, security hardening, and backup setup – processes that often take weeks to coordinate and execute. Your team gets enterprise-grade Oracle Database functionality with cloud-native operational simplicity. No compromise on database capabilities, no sacrifice of security or performance standards, and no extended deployment timelines that delay critical business initiatives. Ready to Transform Your Database Strategy? Oracle@GCP delivers exactly what you need: proven Oracle Database technology with Google Cloud operational excellence. The deployment process is this straightforward, the management is this intuitive, and the results are this reliable. Let’s coordinate on your Oracle@GCP implementation. Your database infrastructure should accelerate your business objectives, not slow them down. Ready to get started? Contact RheoData (cloud@rheodata.com) today to discuss how Oracle@GCP fits your specific requirements. We’ll help you plan the migration, execute the deployment, and optimize your database performance from day one.",
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  "articleBody" : "When doing building and validating an Oracle GoldenGate implementation, understanding the data goes a bit deeper than only using row counts between the source and target databases. Doing validations is essential to having successful GoldenGate implementation and there are a few tools out that can be used to achieve this. These products are: Oracle Veridata (Oracle GoldenGate Tool) – which is great, but a lot of customers do not spring for this options RedGate’s Data Compare for Oracle – is a good tool for smaller datasets, but struggles with larger data sets on comparisons. Customers choose this due to cost Oracle’s DBMS_COMPARISON Package – this is a great, down, and dirty way of getting comparisons within the Oracle database. Limited to the Oracle database though. As I’ve been working on a large scale Oracle GoldenGate implementation for a customer, the need to validate data became apparently very quickly. The customer always relied on row counts as the measure of success; however, as Oracle GoldenGate was processing data, we quickly identified that the data was diverging leading to other data related issues down the line. Since the customer didn’t have Oracle Veridata and RedGate was taking upwards of three hours to compare, we needed a faster solution – DBMS_COMPARISON. The DBMS_COMPARISON package is great tool within the Oracle Database. From looking at the documentation, you can create, compare, and recheck compares as needed. All of which is contained within the database where the comparison is ran from. For an added bonus, compares can be ran over dblinks as well. Making migration comparisons quick and simple. Create Comparison Before any compares can be ran, a comparison has to be created. This is done by using the DBMS_COMPARISON.CREATE_COMPARISON procedure. In this case, we are going to compare two local database (test environment after all). BEGIN DBMS_COMPARISON.CREATE_COMPARISON( comparison_name =&gt; 'TSTUSR_COMPARE_1’, schema_name =&gt; 'TSTUSR’, object_name =&gt; 'RANDOM_LRG’, dblink_name =&gt; null, remote_schema_name =&gt; 'TSTUSR1’, remote_object_name =&gt; 'RANDOM_LRG'); END; / In the create compare statement above, you see that we are creating a compare called “TSTUSR_COMPARE_1”. This name will be referenced in other parts of the comparison setup. Additionally, you see the sourch and target schema and table to compare. The dblink_name is null because we are comparing locally. Run Comparison With the comparison created, now it can be ran. To run a comparison simply run DBMS_COMPARISON.COMPARE procedure. That sounds a bit to simple though. In order to run it, you’ll have to wrap it in a PL/SQL block. SET SERVEROUTPUT ON DECLARE vScanInfo DBMS_COMPARISON.comparison_type; vScanResult BOOLEAN; BEGIN vScanResult := DBMS_COMPARISON.COMPARE( comparison_name =&gt; 'TSTUSR_COMPARE_1’, scan_info =&gt; vScanInfo, perform_row_dif =&gt; TRUE); IF NOT vScanResult THEN DBMS_OUTPUT.put_line('scan_id =' || vScanInfo.scan_id); ELSE DBMS_OUTPUT.put_line('No diffs’); END IF; END; / Reviewing Comparison Notice that we set the server output on. This is so the procedure will return the scan id we need to identify the scan within the database by looking at the DBA_COMPARISON_SCAN view. A simple query like the one below will return all scans from the execution: select * from dba_comparison_scan where scan_id = 4 or parent_scan_id = 4; To find any differences between the tables, the DBA_COMPARISON_ROW_DIFF view can be used. Building on what was done above, the following query can be ran: select * from DBA_COMPARISON_ROW_DIF where scan_id in (select scan_id from DBA_COMPARISON_SCAN where scan_id = 4 or parent_scan_id = 4); This query shows that there is a difference between the tables (STATUS column). In order to find out what is different between the tables, all you have to do is reference the rowid. LOCAL_ROWID is the source record. REMOTE_ROWID is the target record. A query similar to this can be used. select * from tstusr.random_lrg where rowid = 'AAAR5OAAMAAAACVAAA' union all select * from tstusr1.random_lrg where rowid = 'AAAR5QAAMAAAACjAAA'; As you can see, the rows are different by one column (RANDOM_TXT). At this point, you can repair the data (if needed) or let Oracle GoldenGate finish syncing and see if an update statement may have been processed. This post is meant to help you get an understanding that row counts alone during a migration is not going to achive the definition of success. If row counts are 100%, that doesn’t mean the data is 100%. Always drive a bit deeper during migrations to ensure that everything is accounted for. Enjoy!!",
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  "dateModified" : "10/11/2025",
  "datePublished" : "10/11/2025",
  "headline" : "Data comparisons and the DBMS_COMPARISON package",
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  "articleBody" : "Let me be direct: If your AI chatbot told a customer yesterday that your product still costs $99 when you raised prices to $129 last month, you have a $1.2 million problem. That’s what outdated AI responses cost the average enterprise annually in lost revenue, damaged trust, and support escalations. Here’s the reality: Traditional AI models are frozen in time. The moment they finish training, they start becoming obsolete. Meanwhile, your business changes daily—new products, updated policies, fresh compliance requirements. The gap between what your AI knows and what’s actually true is costing you money every single day. The $4.7 Million Question: Retrain or RAG? Option 1: Traditional Retraining Cost: $250,000-$500,000 per cycle Time: 3-6 months Frequency needed: Quarterly (minimum) Annual cost: $1-2 million Result: Still outdated 89 days out of 90 Option 2: Retrieval Augmented Generation (RAG) Initial setup: $75,000-$150,000 Time to deploy: 2-4 weeks Updates: Real-time Annual cost: $200,000-$400,000 Result: Accurate 24/7/365 The math is simple. RAG delivers 5x cost reduction while providing 100% current information. For the love of God, why would anyone choose retraining? How RAG Actually Works (Without the BS) Forget the technical mumbo-jumbo. Here’s what matters: Traditional AI: Like asking your retired employee from 2023 about today’s pricing RAG-Powered AI: Like asking your current sales director who checks the live database RAG transforms your AI from a know-it-all teenager into a strategic advisor who actually verifies facts before speaking. It retrieves real-time data from your authoritative sources—databases, documents, APIs—then generates responses grounded in current reality. Three components. That’s it: Vector Database: Your single source of truth, updated continuously Retrieval Engine: Finds the exact information needed in milliseconds Generation Module: Crafts accurate, contextual responses The Database Advantage: Oracle 23ai and Google AlloyDB Here’s where RheoData’s expertise separates the warriors from the negotiators. Oracle Database 23ai Oracle didn’t just add vector capabilities—they revolutionized them. Native JSON support, built-in vector similarity search, and AI Vector Search mean your RAG implementation runs at speeds that make competitors look like they’re using dial-up. Key advantages: 23x faster vector similarity searches than PostgreSQL Native integration with Oracle’s entire ecosystem Built-in security that passes SOC 2, HIPAA, and PCI compliance without breaking a sweat Automatic indexing that eliminates 67% of manual optimization work Google AlloyDB Google’s PostgreSQL-compatible powerhouse brings its own arsenal: 4x faster analytical queries than standard PostgreSQL Seamless integration with Vertex AI for end-to-end RAG pipelines Automatic storage tiering that cuts costs by 40% Real-time replication with 99.99% availability SLA The strategic play? Use Oracle 23ai for mission-critical, compliance-heavy applications where every millisecond counts. Deploy AlloyDB for cloud-native applications that need to scale elastically with unpredictable demand. Real Results from Real Implementations Financial Services Client (Oracle 23ai RAG) Challenge: Compliance violations from outdated rate information Solution: RAG with real-time regulatory database integration Results: $3.2M in avoided fines, 94% reduction in compliance errors ROI: 426% in year one Healthcare Provider (AlloyDB RAG) Challenge: Outdated treatment protocols in AI-assisted diagnostics Solution: RAG connected to live medical databases and guidelines Results: 47% faster accurate diagnoses, 89% physician satisfaction ROI: $1.8M annual savings in reduced misdiagnoses Retail Giant (Hybrid Oracle/Google RAG) Challenge: Customer service providing wrong product information Solution: Dual-database RAG for inventory and pricing Results: 31% increase in conversion, 78% drop in returns ROI: $4.7M additional revenue in 6 months The Compliance Game-Changer Let’s address the elephant in the boardroom: liability. When your AI hallucinates medical advice, financial recommendations, or legal guidance, you’re not just wrong—you’re exposed. RAG doesn’t just reduce hallucinations; it provides full auditability. Every response traces back to source documents. Every claim links to authoritative data. When regulators come knocking (and they will), you have complete documentation of where every piece of information originated. Oracle 23ai’s blockchain tables provide immutable audit trails. AlloyDB’s point-in-time recovery ensures you can prove exactly what your system knew at any moment. This isn’t just about accuracy—it’s about legal defensibility. Implementation: Days, Not Months Here’s our proven deployment timeline: Week 1: Database architecture and vector schema design Week 2: Data ingestion and vector embedding pipeline Week 3: RAG integration and initial testing Week 4: Production deployment and performance optimization Compare that to the 3-6 month death march of model retraining. We’re talking about transformational capability in less time than your last software upgrade. The Bottom Line Your competitors are either: Burning millions on constant retraining Letting their AI lie to customers Already implementing RAG Which category are you in? RAG isn’t a nice-to-have technology experiment. It’s a strategic imperative that directly impacts revenue, compliance, and customer trust. The question isn’t whether to implement RAG—it’s whether you’ll do it before your competition does. Let’s Get Specific Stop bleeding money on outdated AI. Stop risking compliance violations. Stop letting competitors eat your lunch with better customer experiences. RheoData delivers production-ready RAG implementations that transform your AI from liability to competitive advantage. We’re not consultants who talk—we’re engineers who build. Ready to see RAG in action with your actual data? Contact our Oracle specialists: cloud_oci@rheodata.com Contact our Google Cloud experts: cloud_gcp@rheodata.com We’ll build a proof-of-concept using your data, your use case, and show you exactly what RAG means for your bottom line. No fluff. No promises. Just measurable results. Time to decision: Now!",
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  "dateModified" : "10/11/2025",
  "datePublished" : "10/11/2025",
  "headline" : "Your AI is Lying to Your Customers. Here’s How to Fix It with RAG.",
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```

```json
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    "@type": "BlogPosting",
    "headline": "Manually purging trail files from OCI GoldenGate Service",
    "mainEntityOfPage": "https://rheodata.com/en-us/blog/manually-purging-trail-files-from-oci-goldengate-service",
    "datePublished": "10/11/2025",
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    "articleBody": "Oracle GoldenGate Service is Oracle’s cloud offering to quickly use GoldenGate to move data within OCI as well as other clouds. Now network connection and bandwidth has a bit to do with the speed of data being processed, but it a quick service over all. One thing that any GoldenGate Administrator has to get use to is the lack of access to the underlying host where GoldenGate is running. As my friends, the PMs, have told me this is due to GoldenGate Service being a “SERVICE”. This basically means you do not need or will get access to the underlying filesystem of GGS. For many GoldenGate Administrators this will be frustrating from a troubleshooting aspect – How do you confirm or make sure that trail files are being written to or read from? Well, the answer is in OCI GoldenGate Service, but that is not the point of this post. The item that needs to be discussed is how to clean up trail files in GGS? Cleaning up trail files is important because they do take space and if you don’t have a task enabled to clean up trail files, then space will be consumed and eventually used up. How do you take care of this issue then manually? The answer is simple and what is built into Oracle GoldenGate and Oracle GoldenGate Service – REST APIs. To purge a single set of trail files (all trail files) that begin with a specific name, the below code can be used in Microsoft Visual Studio Code (VSCode). @url = ### POST /services/v2/commands/execute Authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE Content-Type: text/plain { name: purge, purgeType: trails, trails: [ { name: “AL } ], useCheckpoints: false, keep: [ { type: min”, units: files”, value: 0 } ] } ### In the above example code, we are removing all the trail files that being with “AL”. If you want to remove more than one series of trail files, we can simply add more trail file names to the code as follows: @url = ### POST /services/v2/commands/execute Authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE Content-Type: text/plain { name: purge, purgeType: trails, trails: [              {                 name: “AL”               },               {                  “name”:”AB”               } ], useCheckpoints: false, keep: [                {                   type: min”,                    units: files”,                    value: 0                }          ] } ### If you are curious how this would look in a written cURL command, the below would do the same thing: curl --request POST \ --url /services/v2/commands/execute \ --header 'authorization: Basic Z2dhZG1pbjphbHRlY0dHUE9DYWRtaW4yMyE' \ --header 'content-type: text/plain' \ --header 'user-agent: vscode-restclient' \ --data '{name: purge,purgeType: trails,trails: [{name: AL}],useCheckpoints: false,keep: [{type: min,units: files,value: 0}]}' Enjoy!",
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```