Systems | Development | Analytics | API | Testing

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.

The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.

Your team just shipped a microservices refactor. Services are smaller, deployments are faster, and boundaries are clearer. Then, during a design review, someone inevitably suggests: “We should use Kafka.”That suggestion might be the exact architectural breakthrough you need—or it could quietly introduce months of unnecessary operational complexity.This article serves as a practical decision framework.

We are proud to announce the release of Apache Kafka 4.3. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes. With 25 KIPs and over 600 commits since 4.2.0, this release introduces many new features, improvements and bug fixes to all the components. See the Upgrading to 4.3 section in the documentation for the list of notable changes and detailed upgrade steps.

For most go-to-market (GTM) teams, understanding what’s really happening with a customer right now is harder than it should be. Usage data lives in one system, renewals in another, support escalations somewhere else—and field notes are scattered across tools and docs. By the time someone pieces together a full picture, it’s already out of date. As we began using our own data platform internally, this fragmentation became impossible to ignore.

Traditional Customer 360 architectures were perfectly adequate for the era of quarterly reports and static marketing segments. They successfully pooled data from CRMs, transaction logs, and support platforms to build a unified profile. But for GenAI-powered applications? Yesterday's architecture is a massive bottleneck. Here is why legacy systems are breaking down under the demands of modern AI, and how the architecture is forcing a shift to real-time data.

A streaming transformation is a continuous operation that processes events as they arrive, applies logic in real time, and emits transformed results immediately—without waiting for batch jobs to complete. In Apache Flink, a streaming transformation runs continuously, reacting to each event from a stream. This enables real-time data transformation directly on live data.