---
title: RheoData Blog | GoldenGate (7)
description: GoldenGate | RheoData Blog Posts (7)
---

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<https://rheodata.com/en-us/blog/tag/goldengate/page/7#minimal-header__mobile-nav__mmenu>

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- [Customer Stories](https://rheodata.com/customer-stories) 
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- Resources 
    - [Blog](https://rheodata.com/en-us/blog)
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          - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page)
- [Contact](https://rheodata.com/contact)

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<https://rheodata.com/en-us/blog/tag/goldengate/page/7#minimal-header__mobile-nav__mmenu>

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    - [Manufacturing](https://rheodata.com/manufacturing)
    - [Retail](https://rheodata.com/retail)
    - [State & Local](https://rheodata.com/sled)
- [Customer Stories](https://rheodata.com/customer-stories) 
    - [Altec](https://rheodata.com/customer-stories/altec-oci-goldengate-data-migration)
    - [Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration)
    - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication)
    - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation)
- Resources 
    - [Blog](https://rheodata.com/en-us/blog)
    - Books 
          - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page)
- [Contact](https://rheodata.com/contact)

Posts about

# GoldenGate (7)

<https://rheodata.com/en-us/blog/hidden-risk-oracle-to-snowflake>

## [Oracle to Snowflake Monitoring: Stop Replication Blind Spots](https://rheodata.com/en-us/blog/hidden-risk-oracle-to-snowflake)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:06 PM

Last month, we solved a crisis for a retail CIO who called me at 2 AM. His Oracle-to-Snowflake...

[CONTINUE READING](https://rheodata.com/en-us/blog/hidden-risk-oracle-to-snowflake)

<https://rheodata.com/en-us/blog/adminclient-add-credential-doesnt-do-what-you-expect>

## [AdminClient – ADD CREDENTIAL doesn’t do what you expect!](https://rheodata.com/en-us/blog/adminclient-add-credential-doesnt-do-what-you-expect)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:05 PM

Earlier today, I have been working on a few GoldenGate Obey files that will setup a customer’s...

[CONTINUE READING](https://rheodata.com/en-us/blog/adminclient-add-credential-doesnt-do-what-you-expect)

<https://rheodata.com/en-us/blog/goldengate-snowflake-key-pair-auth>

## [Securing Your Oracle GoldenGate to Snowflake Replication: Why Key Pair Authentication Matters More Than You Think](https://rheodata.com/en-us/blog/goldengate-snowflake-key-pair-auth)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:04 PM

When your business operations depend on real-time data flowing from Oracle to Snowflake, the last...

[CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-snowflake-key-pair-auth)

<https://rheodata.com/en-us/blog/different-pipelines-masses>

## [Different pipelines for the masses](https://rheodata.com/en-us/blog/different-pipelines-masses)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:02 PM

If you work in the IT industry long enough, you start to see messaging or wording of items to...

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

<https://rheodata.com/en-us/blog/resolving-ogg-02028-errors>

## [Resolving OGG-02028 Errors](https://rheodata.com/en-us/blog/resolving-ogg-02028-errors)

Posted by [rheostage5132](https://rheodata.com/en-us/blog/author/rheostage5132) | Nov 10, 2025 9:29:01 PM

The error OGG-02028: Failed to attach to logmining server OGG$<extract\_name> error #,### –...

[CONTINUE READING](https://rheodata.com/en-us/blog/resolving-ogg-02028-errors)

<https://rheodata.com/en-us/blog/goldengate-service-ggs-connection-setup>

## [GoldenGate Service (GGS) – Connection Setup](https://rheodata.com/en-us/blog/goldengate-service-ggs-connection-setup)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:00 PM

### Creating Connections

[CONTINUE READING](https://rheodata.com/en-us/blog/goldengate-service-ggs-connection-setup)

<https://rheodata.com/en-us/blog/mimic-ggsci-with-python>

## [Mimic GGSCI with Python](https://rheodata.com/en-us/blog/mimic-ggsci-with-python)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:28:59 PM

Since the release of Oracle GoldenGate 12.2 (Classic) and Oracle GoldenGate 12.3 (Microservices),...

[CONTINUE READING](https://rheodata.com/en-us/blog/mimic-ggsci-with-python)

<https://rheodata.com/en-us/blog/oracle-ai-world-2025-accelerate-ai-initiatives-enterprise-data>

## [Oracle AI World 2025: How RheoData Helps You Accelerate AI with Your Enterprise Data](https://rheodata.com/en-us/blog/oracle-ai-world-2025-accelerate-ai-initiatives-enterprise-data)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:28:58 PM

Oracle AI World 2025 delivered exactly what enterprises need: a clear path to making AI work with...

[CONTINUE READING](https://rheodata.com/en-us/blog/oracle-ai-world-2025-accelerate-ai-initiatives-enterprise-data)

<https://rheodata.com/en-us/blog/data-governance-with-ogg>

## [Data Governance with Oracle GoldenGate](https://rheodata.com/en-us/blog/data-governance-with-ogg)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:20:31 PM

[CONTINUE READING](https://rheodata.com/en-us/blog/data-governance-with-ogg)

<https://rheodata.com/en-us/blog/agnostic-goldengate-adminclient-microservices>

## [Agnostic GoldenGate AdminClient – Microservices](https://rheodata.com/en-us/blog/agnostic-goldengate-adminclient-microservices)

Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:20:30 PM

Back in 2017 when Oracle GoldenGate 12c (Microservices) was released it came with a new command...

[CONTINUE READING](https://rheodata.com/en-us/blog/agnostic-goldengate-adminclient-microservices)

### 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)

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- [vector database consolidation (1)](https://rheodata.com/en-us/blog/tag/vector-database-consolidation)

See all

- <https://rheodata.com/en-us/blog/tag/goldengate/page/6>
- [4](https://rheodata.com/en-us/blog/tag/goldengate/page/4)
- [5](https://rheodata.com/en-us/blog/tag/goldengate/page/5)
- [6](https://rheodata.com/en-us/blog/tag/goldengate/page/6)
- [7](https://rheodata.com/en-us/blog/tag/goldengate/page/7)
- [8](https://rheodata.com/en-us/blog/tag/goldengate/page/8)
- <https://rheodata.com/en-us/blog/tag/goldengate/page/8>

##### About RheoData

 RheoData is based out of Metro Atlanta, GA and provide expert Oracle, Microsoft, Google, and Snowflake services.  Let us  know how we can help!

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##### Contact us

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©RheoData2026. All Rights Reserved.

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  "articleBody" : "Last month, we solved a crisis for a retail CIO who called me at 2 AM. His Oracle-to-Snowflake replication had been running “fine” for three weeks—until his inventory manager noticed their stock reports hadn’t updated since 6 PM the previous day. Twelve hours of missing data. Twelve hours of purchasing decisions based on phantom inventory levels. And nobody knew when the replication had actually stopped. “Bobby,” he said, “we need to make sure this never happens again.” We implemented comprehensive heartbeat monitoring within 48 hours. Problem solved. This situation highlights a critical oversight in most Oracle-to-Snowflake migrations: the assumption that if your replication isn’t throwing errors, it’s working perfectly. But in retail environments where data drives decisions that affect inventory replenishment, pricing strategies, and customer experience, “assumed working” isn’t good enough. The Invisible Problem: Replication Lag Without Visibility When you migrate from Oracle to Snowflake using Oracle GoldenGate, you’re creating a complex pipeline with multiple handoff points. Data flows from your Oracle source through extraction processes, across networks, and into Snowflake through replication processes. At each stage, delays can accumulate—what we call “replication lag.” Here’s what keeps retail leaders awake at night: traditional monitoring only tells you if the replication process is running, not if it’s keeping up. Your dashboards might show green lights while your business decisions are being made on data that’s hours behind reality. Think about it—if your point-of-sale data is lagging by even 30 minutes, and you’re making inventory replenishment decisions based on that delayed information, you could end up with stockouts that lose sales or overstock that ties up working capital. In either case, the cost of poor data timing far exceeds the investment in proper monitoring. Why Standard Monitoring Falls Short Most Oracle-to-Snowflake implementations rely on basic process monitoring: “Is the extract running? Is the replicat applying changes?” But these binary checks miss the nuances that matter for business operations. Consider this scenario: Your GoldenGate extract is running perfectly, capturing every transaction from Oracle. Your Snowflake replicat is also running, applying changes successfully. But a network bottleneck is causing a 2-hour delay between capture and delivery. Standard monitoring says everything is fine. Your business operations are making decisions on 2-hour-old data. This is where heartbeat monitoring becomes crucial. Instead of just checking if processes are running, heartbeat monitoring tells you exactly how long data takes to flow from Oracle commit to Snowflake availability—end-to-end, in real-time. The Heartbeat Solution: Your Early Warning System Heartbeat monitoring works like a pulse check for your data pipeline. Here’s how it provides the visibility you need: Continuous Lag Measurement: Rather than waiting for someone to notice missing data, heartbeat monitoring continuously measures how long transactions take to flow from Oracle to Snowflake. You’ll know within minutes if lag is developing, not hours later when business users start asking questions. Pinpoint Problem Location: When lag does occur, heartbeat monitoring shows you exactly where in the pipeline the delay is happening. Is the Oracle extract falling behind? Is network latency increasing? Is the Snowflake replicat struggling with the workload? This specificity means faster resolution and less downtime. Historical Trending: By maintaining a history of lag measurements, you can identify patterns. Maybe lag consistently spikes during month-end processing, or network performance degrades during peak business hours. This historical view enables proactive capacity planning rather than reactive firefighting. Process Health Detection: Perhaps most importantly, heartbeat monitoring can detect when upstream processes have stopped entirely. If the heartbeat hasn’t updated in your expected timeframe, you know immediately that something has broken—before business users notice missing data. The Technical Foundation (Without the Code Complexity) Implementing effective heartbeat monitoring requires three key components working together: Source Tracking: On the Oracle side, you need a dedicated heartbeat table that gets updated regularly with timestamp information. This table captures when transactions occur and feeds into your replication stream just like your business data. Target Monitoring: In Snowflake, you need both current status and historical tracking tables. The current table shows the latest heartbeat information from each replication process, while the historical table maintains a record of all heartbeat measurements for trend analysis. Automated Processing: Because manual monitoring isn’t realistic in 24/7 manufacturing environments, the system needs automated procedures that calculate lag times, update monitoring tables, and can trigger alerts when thresholds are exceeded. The beauty of this approach is that it uses your existing GoldenGate infrastructure—no additional network connections or monitoring tools required. The heartbeat data flows through the same replication pipeline as your business data, providing an accurate representation of what your actual data experiences. Why This Matters for Retail In retail environments, data timing isn’t just about convenience—it’s about competitive advantage and customer satisfaction. When your inventory management system makes decisions based on delayed sales data, the ripple effects can be significant: Stockouts occur when popular items appear available in your system but have actually sold out hours ago Overstocking happens when “slow-moving” products are actually selling well, but the sales data hasn’t updated Pricing decisions lag behind market conditions when competitor pricing updates don’t flow through in real-time Customer experience suffers when online inventory shows items as available that aren’t actually in stock One client we worked with discovered their overnight inventory reports were consistently 45 minutes behind their actual point-of-sale data. This delay meant their morning inventory managers were making restocking and pricing decisions based on incomplete information from the previous day. After we implemented heartbeat monitoring, they identified that their Snowflake warehouse was scaling down during off-peak hours, causing the replication backlog. A simple configuration change eliminated the lag and improved their inventory accuracy by 23%. The Implementation Challenge While the concept of heartbeat monitoring is straightforward, the implementation requires deep knowledge of both Oracle GoldenGate architecture and Snowflake’s specific data handling characteristics. You need to understand how GoldenGate tokens work, how to properly configure table mappings, and how to create automated procedures that calculate lag accurately across different time zones and system clocks. More importantly, you need to ensure the monitoring solution itself doesn’t impact your production replication performance. Poorly implemented heartbeat monitoring can actually create the problems it’s designed to detect. At RheoData, we’ve solved this challenge multiple times. We know exactly how to implement heartbeat monitoring that provides the visibility you need without creating performance issues or operational complexity. This is where RheoData’s expertise becomes invaluable. We’ve implemented heartbeat monitoring for multiple Oracle-to-Snowflake migrations, and we know the difference between a monitoring solution that provides peace of mind and one that creates new headaches often comes down to implementation details that only come from hands-on experience. Taking the Next Step If you’re running Oracle-to-Snowflake replication without comprehensive lag monitoring, you’re essentially flying blind. You might be fine today, but when problems occur—and in complex data pipelines, they always do—you’ll wish you had implemented proper monitoring before you needed it. The good news is that we can add heartbeat monitoring to existing GoldenGate implementations without disrupting your current operations. It’s an investment in operational confidence that pays dividends every day your systems run smoothly, and proves its worth immediately when issues arise. Ready to eliminate the blind spots in your Oracle-to-Snowflake replication? Let’s spend 15 minutes discussing your current monitoring setup and what comprehensive heartbeat monitoring could mean for your retail operations. I’ll share specific examples of how we’ve implemented this for other retail companies and help you understand what’s involved for your environment. No sales pressure, no generic presentations—just a straightforward conversation about your specific monitoring needs and whether this approach makes sense for your situation. Contact RheoData today: cloud@rheodata.com or (678)-608-1352 Because when your retail operations depend on real-time data, “assumed working” isn’t an acceptable monitoring strategy.",
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  "articleBody" : "Earlier today, I have been working on a few GoldenGate Obey files that will setup a customer’s environment; that is until I ran into an issue with AdminClient. I’m hoping this issue is more of a design overlook than a bug, but let’s see after this post is done. With the shift to everything in the cloud and Oracle GoldenGate moving more and more into microservices, this opens up a remote administration for users. To take this the next step, I built a Docker Container that runs Oracle GoldenGate 21c AdminClient (here). I also wrote about this back in March 2022 (here) . Using AdminClient in this way, I can have a free standing version of AdminClient that allows me to connect to local Oracle GoldenGate implementations, remote or cloud based implementations, and OCI GoldenGate Services. Would highly recommend you take a look at this approach as well. While I was working with AdminClient in writing some obey files, I identified that the below command does something unexpected: connect https://goldengate.rheodata.com deployment dep_ctmsbob_ft2 as oggadmin password WElcome09876^^ ! ADD CREDENTIALS EUDB USER ggate@rd.sub.demovnc.oraclevcn.com:1521/PDBTST_iad1hs.sub.demovnc.oraclevcn.com PASSWORD “WElcome09876^^ The ADD CREDENTIALS command, when ran in a local AdminClient updates the local wallet not the credential store within the deployment. This is documented in the documentation (here): Note: The ADD CREDENTIALS command adds a new username and password to an Oracle wallet that resides on the same system where the Admin Client is running. This credential is used to log in to Oracle GoldenGate Service Manager and Admin Client command line using the CONNECT command. Now that I have a better understanding on where it is running or going to be stored at, lets take a look and see what it would look like: 1. Start AdminClient (Dockerized) docker run -it --rm --memory=2048M --platform linux/amd64 --hostname=GG21c-Admin --name AdminClient rheodata/adminclient:latest 2. Login to the deployment using AdminClient connect https://**********.rheodata.com deployment dep_*******_ft2 as oggadmin password WElcome******** ! 3. Review the credential store -&gt; INFO CREDENTIALSTORE. Notice that I have two credentials already in the credential store. This aligns with what I have in the credential store via the HTML5 interface page as well: At this point, I would think that when I create another credential it will show up in the credential store for the deployment. 4. Add another credential ADD CREDENTIALS EUDB USER ggate@rd.sub.demovnc.oraclevcn.com:1521/***TST_iad1hs.sub.demovnc.oraclevcn.com PASSWORD “WElcome******* This will return successfully an say the credential was added. 5. Check the credential store again -&gt; INFO CREDENTIALSTORE Notice that we do not have an alias in the credential store called EUDB. What this means is that the AdminClient (locally running) cannot directly update the credential store of a deployment. So where is it? Per the documentation, as pointed out earlier, since the AdminClient is running locally, the credential was created locally in a local wallet. After clearing the screen, I can run INFO CREDENTIALS * and will see the alias I would have expected in the deployment. This issue is either an undocumented bug or a gap in the product. I come to this conclusion because if I was to use a standard cURL command against the API for creating credentials, the credentials are added to the deployment without any issue. At this point, I’m going to turn message the team at Oracle and see if they can provide a reason why the product behaves this way. Enjoy!! **** UPDATE **** Follow up from conversation with Oracle – 9/13/2023: After posting this post, I was able to connect with a few friends at Oracle. They pointed out the exact difference with the approach of how the credential store is used. Within the Microservices Architecture (on-premise and cloud), the credential store is pre-allocated and ready to use within the microservices frame work. This means to provide connection details for an alias from a localized AdminClient, we just need to use ALTER CREDENTIALSTORE. If we use the command ADD CREDENTIALS, AdminClient will create a new credential store locally and not update the existing credential store in deployment. The correct command that should be followed when working with a remote deployment (on-premise or cloud), we just need to use ALTER CREDENTIALSTORE. alter credentialstore add USER ggate@rd.sub.demovnc.oraclevcn.com:1521/PDBTST_iad1hs.sub.demovnc.oraclevcn.com alias EUDB domain OracleGoldenGate PASSWORD “********* After execution, the new alias will appear in the AdminSrvr -&gt; Configuration page.",
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  "dateModified" : "10/11/2025",
  "datePublished" : "10/11/2025",
  "headline" : "AdminClient – ADD CREDENTIAL doesn’t do what you expect!",
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  "articleBody" : "When your business operations depend on real-time data flowing from Oracle to Snowflake, the last thing you want is a security breach that shuts down critical processes for days—or worse, exposes sensitive operational data to competitors. Yet many organizations still rely on password-based authentication for their Oracle GoldenGate replication, creating unnecessary risk in an environment where downtime directly impacts the bottom line. The Hidden Risk of Password-Based Authentication Here’s what keeps experienced DBAs awake at night: password-based authentication for database replication creates multiple failure points. Passwords expire, get compromised, or simply fail to rotate properly across environments. When that happens during a critical business window, you’re not just dealing with a technical issue—you’re potentially facing operational delays that affect delivery schedules and customer commitments. We recently worked with a Fortune 500 company whose Oracle GoldenGate replication failed during a scheduled password rotation. The result? Six hours of business data wasn’t flowing to their Snowflake analytics environment, which meant their demand planning team was working with stale data during a critical operational period. The financial impact was immediate and measurable. Why Key Pair Authentication Changes Everything Key pair authentication eliminates the password problem entirely. Instead of relying on credentials that need constant management, your Oracle GoldenGate processes authenticate using cryptographic certificates. This approach provides several critical advantages: Enhanced Security: Certificate-based authentication is significantly more secure than passwords. There’s no credential to steal, no password to guess, and no risk of human error during rotation. Reduced Operational Overhead: Once properly configured, key pair authentication requires minimal ongoing maintenance. No more emergency password resets during production windows. Compliance Readiness: Many enterprise organizations face increasing regulatory scrutiny around data security. Certificate-based authentication demonstrates a mature security posture that auditors expect to see. Elimination of Service Interruptions: Properly implemented key pairs don’t expire unexpectedly, removing a common cause of replication failures during critical business operations. The Implementation Challenge Here’s where many organizations get stuck: configuring key pair authentication between Oracle GoldenGate and Snowflake requires expertise across multiple platforms. Your team needs to understand OpenSSL certificate generation, Snowflake user management, Oracle GoldenGate properties configuration, and JDBC driver requirements. Getting any of these steps wrong can result in authentication failures that are difficult to troubleshoot—especially when you’re under pressure to restore replication quickly. How RheoData Accelerates Your Implementation At RheoData, we’ve implemented key pair authentication for Oracle GoldenGate to Snowflake replication across dozens of enterprise environments. We understand the specific challenges you face: tight maintenance windows, zero tolerance for system disruptions, and the need for configurations that work reliably from day one. Our approach eliminates the trial-and-error that typically accompanies this type of security implementation. We provide: Proven Methodology: Our standardized process ensures all certificate generation, user configuration, and connection parameters are set correctly the first time. Enterprise-Grade Testing: We validate configurations under conditions that mirror your production environment, ensuring reliability when it matters most. Knowledge Transfer: Your team receives comprehensive documentation and training, so you’re not dependent on external support for ongoing maintenance. Fast Implementation: What typically takes weeks of internal research and testing can be completed in days with our expertise. The Business Impact of Getting This Right One of our recent clients, a global enterprise company, saw immediate benefits after implementing key pair authentication for their Oracle GoldenGate replication. They eliminated three password-related outages per quarter, which previously averaged 4.2 hours of downtime each. The improved reliability meant their analytics team always had access to real-time operational data for compliance reporting. More importantly, their IT team could focus on strategic initiatives instead of emergency password management. The peace of mind that comes from knowing your replication is secure and reliable has value that extends far beyond the technical implementation. Ready to Secure Your Replication Environment? If you’re running Oracle GoldenGate replication to Snowflake and want to eliminate password-related risks while improving security posture, we can help you implement key pair authentication quickly and correctly. The conversation starts with understanding your current environment and identifying the best implementation approach for your specific needs. We offer a complimentary 15-minute assessment to review your replication architecture and discuss the fastest path to secure, reliable authentication. Contact us today: Email: cloud@rheodata.com Phone: (678)-608-1352 Don’t let password-based authentication put your critical data replication at risk. The solution is proven, implementable, and more straightforward than you might think—especially with the right expertise guiding the process.",
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  "headline" : "Securing Your Oracle GoldenGate to Snowflake Replication: Why Key Pair Authentication Matters More Than You Think",
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  "articleBody" : "If you work in the IT industry long enough, you start to see messaging or wording of items to repeat itself. In the case, we are going to take a look at the term “pipeline”. But what exactly is a “pipeline”? What is its purpose? How many different variations are there? In this post, we will take a look at the different types of pipelines there seems to be within the IT industry. What is a Pipeline? Data Pipeline In a general sense, a pipeline is a series of operations that are chained together to accomplish a goal. The output of one operation becomes the input of the next operation, until the desired goal is reached. This process can be visualized as a series of pipelines interconnected where data flows from one to the next; along the way data is either quickly moved or manupliated along the way. For example, suppose you want to move data from database A (source) to Kafka (target). The pipeline would look like: Capture all changed data/transactions Ship data/transactions Apply data/transactions to Kafka topic This is an over simplified example of a pipeline that is used to move data from a relational databases to a publication platform; yet it is considered a data pipeline. These types of “pipelines” can be chained together to build a data fabric or data mesh. Tools like Oracle GoldenGate or Oracle Cloud Infrastructure GoldenGate (OCI GoldenGate) can be used to build these pipelines between heterogenous platforms, on-premises and across clouds. Two types of Data Pipelines Batch Processing Batch processing is the most common data pipleline. This was a critical type of pipeline in the early years of the IT industry and enabled organizations to move large amounts of data from one system to another. This type of pipeline is not essential for analtyics, yet it is typically associated with ETL/ELT processes. In many cases, when batch processing is used timing of the execution is not critcal and often happens at night. Stream Processing Stream processing is starting to become the standard in the IT industry today. Through stream processing data is continously updated based on changes that occur between systems; also known as events. Data that is processed through this type of pipeline is stored in topic and can be subscribed to by outside system. Enabling quicker ingestion of data for organizational use. Elements of a Data Pipeline CI/CD Pipeline A Continous Integration/Continous Delivery (CI/CD) pipeline automates software delievery process from a software delievery point-of-view. This type of pipeline does the following: Builds code Runs tests (unit tests, QA tests, etc.) (CI) Deploys new versions of the application (CD) By building CI/CD pipelines, the building and delievery of applications are automated, removes manual errors, provide standard feedback to developers, and enables faster product iterations. Elements of a CI/CD Pipeline A CI/CD pipeline may seem to be more of an overhead process, but it is not. It should be viewed as a runnable specification of steps needed to deliver a software package between versions of releases. Without a CI/CD pipeline, developers and systems administrators would still need to perform the same steps in a manual process, hence being less productive. Most CI/CD pipeliens typically have the following stages: Faiure through any of the stages typically triggers a notification to let the responsible parties know about the cause. Otherwise, the only. notifications are sent after each successful deployment of the application. Machine Learning Pipelines If we take a look at Python with the Scikit-Learn packages, pipelining is used with machine learning processing and resolve issues like data leakage in testing setups. Pipelines, in this case, function by allwoing linear series of data transformations to be linked together resulting in a measurable modeling process. The objective is to gurantee that all phase within a pipeline are limited to the data available within the pipeline. The scikit-learn packages provides built-in functions for building pipelines (sklearn.pipeline &amp; sklearn.make_pipeline), which simplifies the pipeline construction. A typical pipeline for Machine Learning using python with the scikit-learn package may look like the following: Loading Data Data Preprocessing Splitting of data Transformations Predictions and Evaluations The below image illistrates what the pipelien looks like in concept: AI Pipelines AI pipelines or machine learning pipelines (above) are interconnected or streamlined collection of operations. As data works though machine learning systems, the data is stored in collections and used to train models. Essentially, AI pipelines are “workflows” or interactive paths through which data moves through a machine learning platform. An AI pipeline/workflow is generallly made up of the following: Data Ingestion Data Cleaning Preprocessing Modeling Deployment The AI pipeline (workflow) moves information from collection to collection until it reaches the final deployment and represents an iterative process that continously feeds new information to machine learning/AI systems for AIs to learn and process. How does ML Pipelines share AI Pipelines? Understanding what AI Pipelines are, getting an understanding of what is happening is benefitical. There are seveal stages that AI has to work through as part of the “learning” process. These stages are: Preprocessing Learning Evaluation Prediction Each one of these stages are used by an AI as follows: Preprocessing – are the steps and methods that Machine Learning performs during initial modeling. These sub-steps include cleaning data, structuring data, and preparing it for AI learning Learning – is comprised of different models that are generated by Machine Learning algorithms. These different models are: Supervised Learning – providing machine learning algorithms with examples of expected output Unsupervised Learning – machine learning algorithms use data sets to learn about the inherent patterns in the data and how to best use the data for a specific task Reinforement Learning – action-and-reward teaching Deep Learning – teaching that uses layers of neural networks to facilitae machine learning for complext tasks like patter recognition for physical systems, facial and image recognition Evaluation – is driven by a “trained” brain that was created by machine learning algrothms and evaluated against data provided by the end user. At this stage, the AI is execpting to recieve information from the user that matches what the AI has been trained with. The below images illstrates what an AI pipeline looks like in concept: Summary In this artical, we learned that there a few different types of pipelines and that the industry term of “pipeline” can be used for many different topics and discussions. Although the term “pipeline” has become synonomous with many different processes, the core concept is still conveying data through a series of steps while acting upon that data in some way. As the industry and organizations begin to transition into more into the AI/LLM space, having a firm understanding of what type of pipelines are used will benefit and enable organizations to adopt new technolgoies and processes.",
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  "datePublished" : "10/11/2025",
  "headline" : "Different pipelines for the masses",
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```

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  "articleBody" : "The error OGG-02028: Failed to attach to logmining server OGG$ error #,### – ORA-01292 LogMiner for upstream capture cannot find log file – is an indication that the Oracle archive log file that is needed for the extract to start is missing. There are a few ways this can be resolved: 1. Restore the archive log that it is looking for (the easiest but longest way) 2. Rest extract to begin now (doesn’t always word) 3. Figure out what was the last applied transaction and start from there (easiest way but will skip some transactions) This document is not going to focus on option one, instead this will focus on how to identify the last applied SCN and start from there. Extract Status: Before attempting to start the extract, the status of the extract can be seen in the DBA_CAPTURE view. The important columns in this view are – START_SCN, CAPTURED_SCN, APPLIED_SCN, FIRST_SCN, OLDEST_SCN, and FILTERED_SCN. These columns provide details on where the extract is at according to the system change number (SCN). A query to review the timestamps of the SCN in each of these columns, is a follows: select capture_name , (select scn_to_timestamp(39718416866710) from dual) as start_scn , (select scn_to_timestamp(39719410027791) from dual) as captured_scn , (select scn_to_timestamp(39719054864590) from dual) as applied_scn , (select scn_to_timestamp(39718416866710) from dual) as first_scn , (select scn_to_timestamp(39719054864590) from dual) as oldest_scn , (select scn_to_timestamp(39648390214061) from dual) as filtered_scn from dba_capture; If an error is returned for any of these columns, that line in the query should be commented out. When it comes to OGG-02028 error, columns START_SCN, FIRST_SCN, and FILTER_SCN will error out. This is an indication that the extract is off. Last applied transaction: The APPLIED_SCN can be used to identify what was the last transaction applied to the database. In this case, the date of the last applied transaction was 30-JUN-23 11.03.00.00000000 AM. This can be seen by converting the SCN to a timestamp with the SCN_TO_TIMESTAMP function. select scn_to_timestamp(39719054864590) from dual; At this point, we need to locate the archive log that has the transactions from 30-JUN-23 11.03.00.00000000 AM and forward. Locating Archive Log: Once we have the START_SCN and know what the last applied transaction was (APPLIED_SCN), we can attempt to find the archive log needed to start the extract. In this case, we are using the START_SCN of 39718416866710. To query for the archive log need, we use the V$ARCHIVE_LOG view for the FIRST_CHANGE# that matches the START_SCN. SELECT * FROM V$ARCHIVED_LOG where first_change# = 39718416866710 This can be refined a bit more by only looking at the FIRST_CHANGE#, ARCHIVED, and DELETED columns. SELECT first_change#, first_time, archived, deleted FROM V$ARCHIVED_LOG where first_change# = 39718416866710 What this shows is that the extract last applied transaction by the extract was on 30-JUN-23 and was in an archive log dated 26-JUN-23. Additionally, the archive log was deleted, possibility during the last backup cycle. Upon any sort of restart of the extract, it will be looking for a transaction in the middle of the 26-JUN-23 archive log, which will cause the extract not to start with OGG-02028 error. Remedy: The simplest way of restarting an extract that has a missing archive log is to perform an ALTER EXTRACT, SCN . What this will do is reset the extract from the CURRENT_SCN to the CAPTURED_SCN. Allowing the extract to start. Although this is the fastest way to resolve this error, it will lead to a few missing transactions that will be skipped within the redo/archive logs on the source. After the extract restarts successfully, the GoldenGate Administrator needs to do a data comparison across a DBLINK using a look like DBMS_COMPARISION or using Veridata to identify any missing rows and manually sync them. In the end, this is the quickest way to resolve the OGG-02028 error.",
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  "headline" : "Resolving OGG-02028 Errors",
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  "articleBody" : "Creating Connections In Oracle GoldenGate (on-premises) we always have to setup a connection to the database. This is done by using the credential store to house the connection and then testing it. What happens when we move this concept to OCI and the GoldenGate Service (GGS)? The concept of the credential store is still there; however, in GGS it is listed under or considered a “Connection”. Setup a Connection The “Connection” can be setup from the main page of the GoldenGate Service (GGS). It is listed under the “GoldenGate” Sub-menu: This will bring you to the “Connections” page. From here there is a “Create Connection” button. By clicking on the “Create Connection” button, a dialog box opens up from the right-hand side of the screen. This is the details screen where we can provide general information and connection details for making the connection to a database. In the dialog box, provide the general details and select the type of data source that the connection is used for. In this case, we will be connecting to an Oracle Database for this connection. With selecting an Oracle Database and clicking the Next button, we are taken over to “Connection Details”. On this screen, we are presented with two radio buttons that allow us to determine how to connect to an Oracle Database. If we select the “Select database” option, we can select a database that is running in OCI. Or we can select “Enter Database Information”, this option we have to know exactly how we want to connect to the database – think of using a TNSName entry in this case. To simplify this approach and since we are OCI based, we are going to “Select database” option. Notice that connection details are populated for us. All we needed to provide is the database user that we want to connect to. In this case, we are using a “common user” of C##GGATE (above). After which we can click “create”. After clicking “Create”, notice that we are taken to the “Connections Detail” screen. This screen is setting up the connection. Adding Deployment After the connection is created, we can then assign the connection to a “Deployment”. This is done by using the “Assign Deployment” button that is on the Connections page. .By clicking the “Assign Deployment” button, you will be presented with a dialog that comes up in the middle of the page. This dialog box will allow you to select the GGS deployment that the connection is to be assigned to. In this case, we only have one deployment – Atlanta. Testing Connection With the deployment assigned, we can now test the connection. This is done by using the elipsis at the end of the table and selecting “Test Connection”. If everything is correct, i.e password, for the user, a successful connection should be established. A successful connection will look like this:",
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  "dateModified" : "10/11/2025",
  "datePublished" : "10/11/2025",
  "headline" : "GoldenGate Service (GGS) – Connection Setup",
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  "articleBody" : "Since the release of Oracle GoldenGate 12.2 (Classic) and Oracle GoldenGate 12.3 (Microservices), users have been able to interact with Oracle GoldenGate using RESTful APIs. I’ve written about this interaction in previous posts. With the option to use RESTful APIs, the flexibility that is introduced into Oracle GoldenGate is huge. Everything from simple administration to daily monitoring can be scripted. With this in mind, I decided to finish some items I tabled a few years ago (more will come). As I’ve been doing a pure Oracle GoldenGate (Microservices) project recently, it is interesting to see how quickly the number of deployments can multiple. In a single project; running a hub-architecture, there are thirty-two (32) deployments on a single host. Out of these thirty-two deployments, there are eight (8) Adminstration Services with an extract and replicat in each. This means there are sixteen (16) extracts and replicats to be monitored. Being able to quickly view these is key to supporting the environment. Note: Obey files can be written to do something similar, but the down side is that the password has to be passed in clear text within the obey file. With obey files being a bit clunky, scripting languages like Perl, Go, and Python are a natural choice to take over. In this case, I chose Python. By using Python, I’m able to quickly take the REST APIs and consume them. Then have the output displayed similar to what use to be in GoldenGate Service Command Interface (GGSCI) (below). In this format, I can quickly see the status, lag, and lag at checkpoint of the extract and replicats that are running in the deployment. I also added the metric of the last time the process was started. In the end, by using the REST APIs, I or even you can extend Oracle GoldenGate and bend it to your needs. The script that I wrote for this purpose is below. You are more than welcome to use it as written at your own risk. REST API Info If you want more information on the Oracle GoldenGate REST APIs, visit the link here. Python Script To use the below script, you will need to have Python3 installed and then install the “requests” module using the pip install process. #!/usr/bin python # Copyright (c) 2018-2019 Oracle and/or its affiliates. All rights reserved. # Licensed under the Universal Permissive License v 1.0 as shown at http://oss.oracle.com/licenses/upl. # # Since: March 2020/Jan 2023 # Author: Bobby Curtis # Description: Check GoldenGate Processes # # DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS HEADER. # import requests import json import os vUrl = [“https://:”] vAdmin = “” vPass = “” requests.packages.urllib3.disable_warnings() def cls(): os.system(‘clear’) def display_header(url): print(“#############################################################”) print(” GoldenGate Process Status : ” + url) print(“”) print (“#############################################################”) def proc_status (proc_name, url, proc_type): if (proc_type == “extract”): infoUrl = url + “/” + proc_name + “/info/status” #print(infoUrl) response1 = requests.get(infoUrl, verify=False, auth=(vAdmin, vPass)) jsonResponse = response1.json() vStatus = jsonResponse[‘response’][‘status’] vLastStart = jsonResponse[‘response’][‘lastStarted’] vLag = jsonResponse[‘response’][‘lag’] vLagAtCheck = jsonResponse[‘response’][‘sinceLagReported’] print(“Process Name\t” + “Status\t\t” + “Lag\t” + “\tLag At Check\t” + “Last Start Time”) print(proc_name.upper() + “\t\t” + vStatus.upper() + “\t\t” + str(vLag) + “\t\t” + str(vLagAtCheck) + “\t\t” + vLastStart) #print(json.dumps(response1.json(), indent=4)) else: infoUrl = url + “/” + proc_name + “/info/status” #print(infoUrl) response1 = requests.get(infoUrl, verify=False, auth=(vAdmin, vPass)) jsonResponse = response1.json() vStatus = jsonResponse[‘response’][‘status’] vLastStart = jsonResponse[‘response’][‘lastStarted’] vLag = jsonResponse[‘response’][‘lag’] vLagAtCheck = jsonResponse[‘response’][‘sinceLagReported’] print(proc_name.upper() + “\t\t” + vStatus.upper() + “\t\t” + str(vLag) + “\t\t” + str(vLagAtCheck) + “\t\t” + vLastStart) #print(json.dumps(response1.json(), indent=4)) def check_status (proc_type): if (proc_type == “extract”): display_header(xUrl) hubUrl = xUrl + “/services/v2/extracts” #print(hubUrl) else: hubUrl = xUrl + “/services/v2/replicats” #print(hubUrl) response = requests.get(hubUrl, verify=False, auth=(vAdmin, vPass)).text response_info = json.loads(response) #print(response_info) i = 0 while i &lt; len(response_info[‘response’][‘items’]): proc_status(response_info[‘response’][‘items’][i][‘name’], hubUrl, proc_type) i += 1 #Main cls() for xUrl in vUrl: check_status(“extract”) check_status(“replicat”) The script above can be extend to monitor more than one deployment by adding string values to the vURL variable. For every Administration Service plus port number added in the array will return the output for the dpeloyment and the extracts and replicats that is running there. What this output will not show you the status of the Oracle GoldenGate Services that would be seen through AdminClient. Meaning there is a give-n-take on the approach you are trying to monitor with this python script. There are more improvements that can be done to this script, but for the project where this will be use it provides the needed output. Enjoy!!",
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  "articleBody" : "Oracle AI World 2025 delivered exactly what enterprises need: a clear path to making AI work with their existing data infrastructure. The announcements coming out of Las Vegas this year aren’t just impressive from a technical standpoint—they’re strategically significant for any organization that’s been asking the question: “How do we actually implement AI with our enterprise data?” Oracle’s answer is now crystal clear, and at RheoData, we’re ready to help you execute on that vision. Let me share what Oracle announced and, more importantly, how RheoData can help your organization leverage these innovations to accelerate your AI initiatives. The Oracle ACE Community: Where Expertise Meets Innovation One of the highlights of Oracle AI World was reconnecting with the Oracle ACE community—a network of technical experts who share knowledge and drive innovation across the Oracle ecosystem. At RheoData, we’re proud to be part of this community and bring that same collaborative spirit to every client engagement. The Oracle ACE Apprentice program continues to grow, offering professionals at all levels the opportunity to expand their skills and build their networks. This commitment to knowledge sharing is exactly what makes Oracle technologies so powerful—there’s an entire community dedicated to helping organizations succeed. When you work with RheoData, you’re not just getting a vendor. You’re getting partners who are deeply embedded in the Oracle community, who understand the latest innovations, and who have direct connections to the experts driving these technologies forward. Oracle Database 26ai: AI Architected Into Your Data Here’s the game-changer: Oracle Database 23ai has become Oracle Database 26ai, and this transformation represents Oracle’s bold vision of architecting AI directly into the core of data management. What Oracle announced: Oracle AI Database 26ai replaces Oracle Database 23ai with a massive expansion of AI capabilities across AI Vector Search, Agentic AI, AI management, AI Data Development, AI Analytics, and AI App Development. This is Oracle’s “AI for Data” strategy in action—making AI simple to learn, simple to use, and integrated throughout your entire data stack. The transition is remarkably straightforward. If you’re running 23ai, you apply the October 2025 quarterly Release Update. No complex upgrade. No application recertification. If you’re on 19c or 21c, you can upgrade directly to 26ai. For Autonomous Database customers, the upgrade happens automatically. Why this matters for your business: Your enterprise data is your most valuable asset, and now Oracle has made it AI-ready without requiring you to move data, rebuild applications, or sacrifice the reliability your business depends on. Oracle AI Database 26ai enables you to run dynamic agentic AI workflows that combine your private database data with public information to deliver sophisticated insights and actions. Advanced AI features like AI Vector Search are included at no additional charge. The platform supports the tools and frameworks your team wants to use—Apache Iceberg, Model Context Protocol, industry-leading LLMs, popular agentic AI frameworks, and ONNX embedding models. How RheoData accelerates your Database 26ai journey: At RheoData, we specialize in Oracle Database transformations and AI strategy. Here’s what we bring to your Database 26ai initiative: Strategic Assessment: We evaluate your current database environment and create a clear roadmap for leveraging Database 26ai’s AI capabilities Seamless Migration: Whether you’re upgrading from 19c, 21c, or 23ai, we ensure a smooth transition with zero downtime strategies AI Use Case Development: We help you identify and implement high-value AI use cases that leverage your existing enterprise data Performance Optimization: Our team ensures your Database 26ai deployment is architected for maximum performance and scalability Ongoing Support: We provide the expertise you need to continuously evolve your AI capabilities as Oracle releases new features We’ve guided numerous organizations through complex Oracle Database transformations, and we understand how to make these initiatives successful while minimizing disruption to your operations. Oracle GoldenGate: The Real-Time Data Foundation for AI Oracle GoldenGate has always been the gold standard for real-time data replication and integration, and the announcements at AI World 2025 reinforce why it’s essential for organizations building AI-ready data platforms. Key GoldenGate capabilities announced: Certifications for Oracle AI Data Platform and Autonomous AI Lakehouse — GoldenGate now integrates seamlessly with Oracle’s AI-optimized environments, eliminating ETL complexity and accelerating time to insights. GoldenGate for MongoDB Migrations 26ai — Expanding GoldenGate’s any-to-any integration capabilities for heterogeneous data environments. Enhanced Multicloud Architecture — Deploy GoldenGate across OCI, AWS, Azure, and Google Cloud with consistent performance and reliability. Tighter Integration with Database 26ai — Support for vector data types, JSON Relational Duality Views, and GoldenGate Data Streams enables real-time, event-driven AI architectures. Air-Gapped Replication — Deploy in Oracle Network Security Group environments for organizations with the strictest security requirements. GoldenGate Free — Enterprise-grade replication technology available for development, testing, and proof-of-concept projects. Why GoldenGate is critical for your AI strategy: AI requires fresh, accurate data. GoldenGate provides real-time change data capture and replication that ensures your AI models and analytics always work with current information. Whether you’re building machine learning pipelines, real-time analytics dashboards, or agentic AI applications, GoldenGate creates the data foundation that makes these initiatives successful. How RheoData implements GoldenGate for AI success: RheoData has deep expertise in Oracle GoldenGate across all deployment scenarios—on-premises, cloud, and hybrid. Here’s how we help organizations leverage GoldenGate for their AI initiatives: Real-Time Data Architecture: We design and implement GoldenGate solutions that feed your AI platforms with continuously updated data Multicloud Integration: We connect your Oracle databases with Azure, AWS, and Google Cloud analytics and AI services using GoldenGate High Availability: We architect GoldenGate deployments that ensure your data replication never becomes a single point of failure Performance Tuning: We optimize GoldenGate for maximum throughput and minimal latency in high-volume environments Migration Expertise: We use GoldenGate to enable zero-downtime migrations to cloud platforms while maintaining business continuity Our team has successfully implemented GoldenGate for organizations processing millions of transactions daily, and we understand the nuances of making real-time replication work at enterprise scale. OCI GoldenGate on Azure: Breaking Down Cloud Barriers The general availability of OCI GoldenGate integration with Oracle Database@Azure is transformational for organizations committed to Azure as their cloud platform. What this integration enables: Organizations can now run OCI GoldenGate natively in Azure data centers and manage it through the Azure console, providing seamless real-time data synchronization between Oracle databases and Azure services like Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage, Azure Event Hubs, and Microsoft Fabric. The integration delivers minimal latency, unified management through Microsoft Entra ID, and creates true real-time AI pipelines by combining GoldenGate’s change data capture with Azure AI services. Oracle Database Mirroring in OneLake (now in public preview) provides low-cost, low-latency mirroring of Oracle databases directly into Microsoft Fabric’s OneLake in Delta Lake–optimized format, eliminating complex ETL pipelines. How RheoData maximizes your Azure + Oracle investment: At RheoData, we specialize in multicloud architectures that leverage the best of both Oracle and Microsoft technologies. Here’s how we help: Azure Integration Strategy: We design solutions that seamlessly integrate Oracle Database@Azure with your existing Azure ecosystem GoldenGate on Azure Implementation: We deploy and configure OCI GoldenGate in Azure for optimal performance and reliability Microsoft Fabric Integration: We connect your Oracle data to Microsoft Fabric, Power BI, and Copilot Studio for comprehensive analytics and AI Cost Optimization: We architect solutions that maximize value while minimizing data movement costs across cloud platforms Security and Compliance: We ensure your multicloud data flows meet your organization’s security and regulatory requirements We understand both the Oracle and Azure ecosystems deeply, and we know how to make them work together to deliver measurable business value. Your Data. Your AI. Our Expertise. Oracle AI World 2025 made one thing abundantly clear: the AI revolution isn’t about replacing your existing data infrastructure—it’s about making that infrastructure AI-ready. Oracle Database 26ai and the enhanced GoldenGate platform provide the technical foundation. What you need now is a partner who can help you execute on that vision with your specific data, your unique business requirements, and your strategic objectives. That’s where RheoData comes in. We bring decades of combined experience in Oracle Database, Oracle GoldenGate, cloud architecture, and AI strategy. We’ve helped organizations across industries transform their data platforms to support real-time analytics, machine learning, and AI-driven decision making. We understand the technical complexities, the organizational challenges, and the business imperatives that drive these initiatives. What makes RheoData different: Deep Oracle Expertise: We’re Oracle technology specialists with extensive experience in Database, GoldenGate, and OCI AI Strategy Focus: We don’t just implement technology—we help you identify and execute on high-value AI use cases Multicloud Proficiency: We architect solutions across Oracle Cloud, Azure, AWS, and Google Cloud based on your needs Results-Driven Approach: We focus on delivering measurable business outcomes, not just technical implementations Partnership Mindset: We work alongside your team, transferring knowledge and building capabilities for long-term success Ready to Accelerate Your AI Initiatives? The technologies announced at Oracle AI World 2025 create unprecedented opportunities for organizations to leverage AI with their enterprise data. The question isn’t whether to pursue these opportunities—it’s how to execute effectively. RheoData has the expertise, experience, and strategic approach to help your organization: ✓ Upgrade to Oracle Database 26ai and unlock AI capabilities with your existing data ✓ Implement Oracle GoldenGate for real-time data integration across your enterprise ✓ Integrate Oracle technologies with Azure, AWS, or Google Cloud for multicloud AI platforms ✓ Design and deploy AI-ready data architectures that scale with your business ✓ Build high-value AI use cases that deliver competitive advantages Let’s start the conversation. If you’re ready to transform your enterprise data into an AI-ready strategic advantage, we’re ready to help you execute on that vision. Contact RheoData today: 📧 cloud@rheodata.com Our team will work with you to understand your objectives, assess your current environment, and create a clear roadmap for leveraging Oracle Database 26ai, GoldenGate, and AI technologies to achieve your business goals. The AI revolution is here. Your data is ready. Let’s execute together.",
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  "articleBody" : "Introduction Data governance is crucial for maintaining data quality, security, and compliance. Oracle GoldenGate, a comprehensive data integration and replication software, plays a significant role in enforcing and supporting various data governance policies. How Oracle GoldenGate Supports Data Governance Oracle GoldenGate provides several features that aid in data governance: Data Replication and Distribution: Ensures data consistency across multiple systems, which is vital for maintaining a single source of truth. Data Transformation: Allows for data cleansing and standardization during replication, improving data quality and adherence to standards. Data Auditing and Tracking: Captures changes to data, providing an audit trail for compliance and accountability. Real-Time Data Delivery: Enables timely access to accurate data, supporting informed decision-making. Security Features: Includes encryption and secure data transfer, protecting sensitive information during replication. Key Features and Their Governance Implications Benefits of Using Oracle GoldenGate for Data Governance Improved Data Quality: Data transformation capabilities ensure consistent and accurate data across systems. Enhanced Data Security: Encryption and secure transfer protocols protect sensitive data. Regulatory Compliance: Audit trails and data tracking support compliance with various regulations. Efficient Data Management: Real-time replication and distribution streamline data management processes. Increased Data Accessibility: Timely data delivery enables better decision-making. Conclusion Oracle GoldenGate is a powerful tool that significantly supports data governance efforts. Its features for data replication, transformation, auditing, and security contribute to maintaining high data quality, ensuring compliance, and enabling efficient data management. By leveraging Oracle GoldenGate, organizations can establish a robust data governance framework that promotes trust and reliability in their data.",
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  "articleBody" : "Back in 2017 when Oracle GoldenGate 12c (Microservices) was released it came with a new command line tool called AdminClient. Over the course of time, I’ve written about how to use AdminClient but never covered a simple fact of how do you get access to it. By default, if you want access to AdminClient; you have to install it and access via Oracle GoldenGate Home on the host where it is installed. Which is great if you are still working with the concept of SSHing into a host to administer Oracle GoldenGate (counter productive to what Oracle intended with Microservices). In many conversations that I had over the years, I mentioned that the AdminClient can be installed and used remotely from a Windows workstation or a remote Linux machine. Which is true, but how is this done? At the same time, many consultants and GoldenGate admin who prefer the Mac OS were left with no option other than SSHing into the GoldenGate host. How do you address these concerns? Install Oracle GoldenGate (Microservices) locally Install Oracle GoldenGate (Microservices) in a container Let’s address the first option – Install Oracle GoldenGate (Microservices) locally In order to access GoldenGate’s AdminClient from your Windows workstation or a remote Linux machine, you have to: Download the complete Oracle GoldenGate (Microservices) binary set – Windows or Linux (x86-64) Install the complete binary set into a local Oracle GoldenGate Home ($OGG_HOME) Access AdminClient from a local Oracle GoldenGate (Microservices) Home from your local command line tool (DOS or Shell prompt) This is similar to installing the Oracle Client or SQLcl for database access. However, unlike Oracle Client or SQLcl, you have to install “ALL” of the Oracle GoldenGate (Microservices) binaries. This means you have to have at least ~3G+ of space on your machine taken up in order to us AdminClient (a bit of a waste). Let alone, Mac OS users still do not have access to AdminClient locally although Mac OSX is based in BSD Linux. For a tool that is meant to make administration of Oracle GoldenGate simpler, there is a lot of overhead that has to be considered on your local Windows workstation or remote Linux machine. Not to mention the complete lock out of users using a Mac OS platform. There is hope though! With a bit of guidance and understanding, you can get the AdminClient to run across all platforms that are supported including the Mac OS. To address the second option RheoData has taken the time to develop a Docker container that will give you access to the AdminClient by simply deploying a container. At the moment, this container is available on RheoData’s public Docker Hub. Do expect this image to be updated over time with more details, but at the moment it is available for usage. To use this containerized version of Oracle GoldenGate (Microservices) AdminClient, do the following: 1. Pull the latest release $ docker pull rheodata/adminclient 2. Issue ‘docker run’ $ docker run -ti –rm rheoData/adminclient:latest 3. Use AdminClient as normal Login to an Oracle GoldenGate (Microservices) environment remotely. In the image below, we are accessing an Oracle GoldenGate environment that is currently running on a compute node in Oracle Cloud Infrastructure. By placing Oracle GoldenGate (Microservices) AdminClient into a container, a user can now deploy AdminClient on all major platforms that a traditionally used (Windows, Linux, and Mac OS). At the same time, this finally puts the command line option for Oracle GoldenGate (Microservices) in a read to use state that can be quickly deployed and used in a wide range of environments – on-premises, cloud, or hybrid. Now the only thing that needs to be done is for Oracle to actually create a smaller footprint installer for the AdminClient only. Until then feel free to download and use this to administer Oracle GoldenGate (Microservices).",
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