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This release introduces a new unified documentation hub - Factor House Docs. It also introduces major data inspection enhancements, including comma-separated kJQ Projection expressions, in-browser search, and over 15 new kJQ transforms and functions. Further improvements include more reliable cluster monitoring with improved Under-Replicated Partition (URP) detection, support for KRaft improvements, the flexibility to configure custom serializers per-cluster, and a resolution for a key consumer group offset reset issue.

Kpow 94.5 enhances data inspection with comma-separated kJQ Projection expressions, in-browser search, and flexible deserialization options. This release also adds high-performance streaming for large datasets and expands kJQ with new transforms and functions—testable on our new interactive examples page. These updates provide deeper insights and more granular control over your Kafka data streams.

Kpow now offers enhanced under-replicated partition (URP) detection for more accurate Kafka health monitoring. Our improved calculation correctly identifies URPs even when brokers are offline, providing a true, real-time view of your cluster's fault tolerance. This helps you proactively mitigate risks and ensure data durability.

We're excited to launch the new Factor House Docs, a unified hub for all our product documentation. Discover key improvements like a completely new task-based structure, interactive kJQ examples, and powerful search, all designed to help you find the information you need, faster than ever. Explore the new home for all things Kpow, Flex, Factor Platform and more.

This guide demonstrates how to enhance Kafka monitoring and data governance by integrating Kpow's audit logs with external systems. We provide a step-by-step walkthrough for configuring webhooks to send real-time user activity alerts from your Kafka environment directly into collaboration platforms like Slack and Microsoft Teams, streamlining your operational awareness and response.

This project transforms the static "theLook" eCommerce dataset into a live data stream. It uses a Python generator to simulate user activity in PostgreSQL, while Debezium captures every database change and streams it to Kafka. This creates a hands-on environment for building and testing real-time CDC pipelines.

Apache Kafka 4.1 has landed: with queue support in preview, improved Kafka Streams coordination, and new security and metrics features, this release marks a major milestone for the future of real-time data systems.

From structuring data streams to spinning up full pipelines locally, our latest Kafka x Flink meetup in Melbourne was packed with hands-on demos and real-time insights. Catch the highlights and what's next.

Factor House has opened a public Slack for anyone working with streaming data, from seasoned engineers to newcomers exploring real-time systems. This space offers faster peer-to-peer support, open discussion across the ecosystem, and a friendly on-ramp for those just getting started.

Learn how to build a real-time "Top-K" analytics pipeline from scratch using a modern data stack. This open-source project guides you through using Apache Kafka, Apache Flink, and Streamlit to ingest, process, and visualize live data, turning a continuous stream of events into actionable insights on an interactive dashboard.