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The partnership between Oracle and Google Cloud represents one of the most significant collaborative achievements in enterprise technology. Oracle Database@Google Cloud Platform brings together Oracle’s proven database excellence with Google Cloud’s industry-leading infrastructure and AI capabilities, creating unprecedented opportunities for enterprise digital transformation. This strategic
When IT executives evaluate Oracle workloads for Microsoft Azure migration, the conventional wisdom points toward SQL Server as the natural destination. However, this assumption overlooks a critical business reality: Oracle@Azure delivers superior ROI while preserving your technology investments. RheoData’s enterprise migration analysis reveals why
The enterprise data landscape just shifted fundamentally. Oracle Database 23ai isn’t simply another version release—it’s the convergence point where decades of enterprise database excellence meets the transformative power of artificial intelligence. After working with this technology since its first beta release, I can tell you
Model Context Protocol (MCP) servers are revolutionizing enterprise AI by bridging the gap between artificial intelligence and business-critical applications, but this powerful connectivity creates significant security challenges. While basic HTTPS encryption provides a foundation, enterprise MCP deployments require comprehensive security architecture that protects sensitive data across multiple systems. This guide provides executives and IT professionals with essential security components—from authentication and network isolation to monitoring and incident response—plus a practical 12-point implementation checklist that ensures your MCP infrastructure delivers both innovation and enterprise-grade protection.
Remember when everyone said the cloud was just a fad? We’re hearing similar skepticism about AI in enterprise data management today. But here’s what I’ve learned after thirty years in technology leadership: the organizations that dismiss transformative technologies early often spend the most catching up
Your organization sits on valuable data trapped in operational silos, while competitors leverage real-time insights to capture market opportunities. Traditional batch ETL processes create 24-hour data delays that render AI models reactive rather than predictive, causing missed revenue opportunities and increased operational risk. The Solution:
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
Retrieval Augmented Generation (RAG) represents a significant advancement in natural language processing that addresses fundamental limitations in static language models. By combining the generative capabilities of large language models with dynamic information retrieval systems, RAG enables AI systems to access and incorporate external knowledge during
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
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
Artificial Intelligence (AI) is rapidly transforming the business landscape, offering unprecedented opportunities for innovation and growth. However, successful AI implementation requires careful planning and execution. This article explores key strategies that enterprises can leverage to effectively integrate AI solutions into their operations. By establishing 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
Introduction This document outlines the benefits of leveraging Oracle GoldenGate for replicating vectors to enhance AI applications. Oracle GoldenGate provides real-time data replication, enabling AI models to access the most current and relevant information. This capability is crucial for applications requiring up-to-date insights, such as
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