The Context Gap caused by fragmented tools hinders effective Kafka monitoring and troubleshooting, as it forces engineers to manually piece together logs and metrics. This guide demonstrates how to close that gap using Kpow's unified workflow to identify the stall, inspect the data, and resolve the incident in a single interface.
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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 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.
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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.
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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.
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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.
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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.
We’re building more than products, we’re building a community. Whether you're getting started or pushing the limits of what's possible with Kafka and Flink, we invite you to connect, share, and learn with others.
