Systems | Development | Analytics | API | Testing

Why Cloudera Data in Motion? #RealTimeAI #DataInMotion

Unlock the full potential of your data fabric and accelerate your AI journey with Cloudera Data in Motion. Many organizations struggle with massive amounts of diverse data spread across different formats, vendors, and locations—whether in the cloud or on-premises data centers. Cloudera provides the scalable, performant data services needed to move and process this information in real-time. Discover how Cloudera’s open-source approach can help you unlock the power of your data anywhere.

Stop Saying "Data Governance." Say This Instead.

Stop saying "Data Governance." Start saying Data Enablement. If your team thinks governance is just red tape, you’re doing it wrong. True governance is a foundation of accountability that ensures high-quality data flows everywhere. Bring your team along on the journey. Show them that a little bit of process right now means they get to make decisions faster and better tomorrow. Trust the data. Speed up the business. Learn more from Swire Coca-Cola's Bharathi Rajan on podcast.

How Xray's AI Test Prioritization Helps Teams Focus on High-Risk Tests

Test execution is one of the most time-sensitive stages of software delivery. Teams are expected to validate functionality, ensure stability, and support release decisions within increasingly shorter development cycles. Even with strong automation in place, there is rarely enough time to execute every Test before a release. This makes prioritization a critical part of the QA process.

Tableflow: Turn Kafka Topics into Iceberg Tables

TL;DR: Tableflow is a Confluent Cloud feature that materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables, eliminating custom data pipelines by automatically handling schematization, type conversions, schema evolution, CDC stream materialization, catalog publishing, and table maintenance.

Redpanda vs Kafka vs Confluent: An Honest Comparison

Data streaming has moved from a niche pattern used by a handful of internet-scale companies to the default backbone for event-driven architectures, real-time analytics, and now AI pipelines. What started as log aggregation at LinkedIn has become the plumbing for fraud detection, IoT telemetry, microservices communication, and retrieval-augmented generation. Three names dominate that conversation today.

Proving ROI on On-Premises BI: Quantify Data Security Value for CFOs and CIOs

Most teams can explain why sensitive BI data should stay on-premises. Far fewer can explain what that decision is worth in dollars. That gap matters. IT can see the control benefits. Finance wants numbers. Executives want a simple answer: what risk drops, what costs change, and what value shows up over 3 to 5 years? This is where a business case beats a technical pitch. On-premises BI can protect sensitive data, support compliance, and give teams direct control over hosting.

News Analysis 2026: How AI Performance Testing Tools Are Transforming Software Quality

This summer, leading performance testing platforms have introduced a new wave of AI capabilities that are fundamentally changing how software teams validate application speed and reliability. Rather than incremental updates, these advances mark a step change: AI performance testing now enables faster test creation, greater accuracy, and wider accessibility across teams.

Salesforce MCP: Is CRM Data Enough for Your AI Agent?

Connecting Salesforce to Claude via MCP is the advancement the SERP says it is. You authenticate once, your AI agent queries live CRM data, and you stop copying deal records into chat windows. For a Revenue Operations Manager who spent Q1 begging an admin to export pipeline snapshots, that matters.