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
This week, I found myself deep in a familiar challenge – helping a client navigate the complexities of replicating Oracle Large Objects (LOBs) to Snowflake using Oracle GoldenGate. What started as a seemingly straightforward data integration project quickly revealed the nuanced technical considerations that separate
Modern AI and machine learning initiatives demand fresh data to deliver accurate predictions and actionable insights. While traditional batch ETL processes served us well in the past, today’s AI models require continuous data streams to maintain relevance and accuracy. The retail and supply chain industries
Your Snowflake bills are probably 3x higher than they need to be. We wrapped up an implementation where we slashed compute costs by 70% while improving replication performance by 67%. The secret? Properly configured Oracle GoldenGate 23ai performance settings specifically tuned for X-Small Snowflake warehouses.
I’ve seen three DBAs lose their jobs over Oracle GoldenGate configuration losses in the past two years. Not performance issues. Not design flaws. Simple backup failures. The 3 AM Call That Changes Everything Picture this: Your phone rings at 3 AM. Production replicat REPPROD01 is
Oracle GoldenGate doesn’t have a native high availability solution. For a tool that’s mission-critical to your data replication strategy, that’s a problem you can’t ignore. The Real Cost of No Native HA Every CTO and IT Director knows the drill. Your GoldenGate environment goes down,
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
“Our Oracle databases are drowning in decades of business data, and our analysts are spending more time waiting for reports than analyzing them. We need to get this data into Snowflake for real-time analytics, but we can’t afford any disruption to our production systems.” Sound
The $73 Million Question Every Executive is Asking: “Why Isn’t Our Snowflake Investment Paying Off?” Look, I’m going to tell you something that’ll change how you think about analytics. Your competitors aren’t beating you because they have better data. They’re beating you because their Oracle
June is designated as Post-Traumatic Stress Disorder (PTSD) Awareness Month, a period dedicated to acknowledging and providing support for those afflicted with this condition. Drawing upon experiences as a military veteran and current Owner/Founder role, I have witnessed the substantial impact of PTSD, which
As we approach AI projects, the statistics associated need to be viewed through a critical and strategic lens. The high initiation rate reflects the undeniable importance of AI for future competitiveness. However, the alarmingly high failure rate demands immediate attention. We must move beyond the
Google Next ’25 and AlloyDB Innovations The recent Google Next ’25 conference provided a deep dive into the Google Ecosystem, with a standout session focusing on AlloyDB’s latest advancements. Two particularly noteworthy innovations in both AlloyDB (cloud) and AlloyDB-Omni (on-premise) are poised to make a
This post we are going to look at some items related to getting started with Generative AI. This is mostly related to how I got started looking at GenAI and what I presented this year at the Rocky Mountain Open User Group (RMOUG). The presentation
Artificial intelligence (AI) is transforming how businesses operate, and one of the most exciting areas is Retrieval Augmented Generation (RAG). RAG allows AI models to answer questions and generate content by accessing and referencing external data, making them far more accurate and relevant. But for
Large Language Models (LLMs) have revolutionized many aspects of technology and business. However, the effectiveness and accuracy of these models are heavily reliant on the quality and currency of the data they are trained on. Feeding LLMs with fresh data is not just a best
Data governance and artificial intelligence (AI) are two powerful forces shaping the future of organizations. When combined, they offer immense potential but also introduce complex challenges. RheoData, the subject matter experts, will outline the positives and negatives of this intersection. Positives of Data Governance and
As AI Experts,RheoData understands the increasing integration of Artificial Intelligence (AI) into various aspects of business operations. With this integration comes the critical need to address AI ethics, particularly how AI should be used without bias to accomplish tasks set out by management. This post
Building a Data Governance Framework Data governance is crucial for any organization looking to leverage its data effectively. It provides a structured approach to managing, using, and protecting data assets. Let’s break down how to build a data governance framework in a simple and understandable
Data Governance with Oracle GoldenGate This document outlines how Oracle GoldenGate contributes to and supports data governance practices within an organization. Introduction Data governance is crucial for maintaining data quality, security, and compliance. Oracle GoldenGate, a comprehensive data integration and replication software, plays a significant
Data Governance Explained Data governance is the overall management of the availability, usability, integrity, and security of data in an organization. In simpler terms, it’s about making sure the right people have the right data at the right time, and that the data is accurate