Systems | Development | Analytics | API | Testing

Introducing AI Transport v0.5.0: durable execution with Steps

AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.

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.