Blog

Axual 2025.3 release introduces KSML 1.1 integration for automated stream processing deployment, group-based resource filtering for multi-team governance, and experimental MCP Server for AI-driven platform operations. Includes JSON schema support, Protobuf processing (beta), and enhanced audit tracking for enterprise Kafka implementations.

The Axual 2025.2 summer release delivers targeted improvements for enterprise-grade Kafka deployments. In this post, we walk through the latest updates—from enhanced audit tracking and OAuth support in the REST Proxy to smarter stream processing controls in KSML. These features are designed to solve the real-world governance, security, and operational challenges enterprises face when scaling Kafka across teams and systems.

Axual 2025.1 is here with exciting new features and updates. Whether you're strengthening security, improving observability, or bridging old legacy systems with modern event systems, like Kafka, Axual 2025.1 is built to keep you, your fellow developers, and engineers ahead of the game.

Consumer group offsets are essential components in Apache Kafka, a leading platform for handling real-time event streaming. By allowing organizations to scale efficiently, manage data consumption, and track progress in data processing, Kafka’s consumer groups and offsets ensure reliability and performance. In this blog post, we'll dive deep into these concepts, explain how consumer groups and offsets work, and answer key questions about their functionality. We'll also explore several practical use cases that show how Kafka’s consumer groups and offsets drive real business value, from real-time analytics to machine learning pipelines.

Apache Kafka is a powerful event-streaming platform, but does your enterprise need to go all in from day one? In this blog, we explore why starting small with Kafka is the best strategy. Learn how an incremental approach can help you reduce complexity, and scale efficiently as your needs grow. Whether you're new to Kafka or looking for a practical implementation strategy, this guide will set you on the right path.

Kafka Consumer Configuration is at the heart of building efficient, scalable, and reliable data streaming applications. Whether you’re working with event-driven architectures, batch data ingestion, or real-time stream processing, the right configurations can make all the difference. In this guide, we’ll explore the most important Kafka consumer settings, break down their impact, and showcase practical use cases to help you optimize performance. By the end, you’ll have a clear roadmap to fine-tune your Kafka consumers for maximum efficiency.