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If you replicate data between two distinct Kafka clusters, you already know the payloads can match while the offsets might not. This post is about how K2K 2.0 now also keeps consumer committed offsets in sync between the source and the target so consumer groups can fail over in a Disaster Recovery situation, avoiding large re-reading of data or row skips. This offers the community more choice for DR than just MirrorMaker2 and Confluent solutions have until now.

Summary Kafka has become the backbone of the real-time enterprise. The streams it carries are not only time, but business critical: a fraud event isn't processed, a sales order not fulfilled, a trade not settled. Yet we heard a recurring theme from Kafka teams: their business is running critical streaming applications without proper Kafka resiliency.

Citizens today compare government digital services to the best experiences they have in the private sector. A missed benefit payment. A tax portal that crashes during filing season. A license renewal that takes weeks instead of minutes. Today, software failures aren’t just IT problems, they’re public trust problems, and many state and local agencies are struggling to deliver digital services quickly, effectively, and consistently.

Companies already spend more than $1 trillion a year trying to catch bad code before it ships. AI is making that job a lot more difficult.

Test automation tells you what happened. Release assurance empowers you to decide what to do about it, closing the loop from business intent to production confidence.

Automated data integration enables AI and analytics based on mission-critical operational data stored in IBM Z.

Interoperability means the optionality to adapt to rapidly changing future demands.

A Node.js application can continue starting, accepting traffic, and passing health checks long after the runtime underneath it has become unsupported. That creates a dangerous assumption: In reality, “running” and “supported” are two very different states. A Node.js release is more than a JavaScript executable.

Platform teams want to maximize the value of data in motion, but separate workstreams for stream processing, monitoring, KRaft migrations, and data governance create friction as environments grow. As a result, teams spend more time managing operational overhead and less time building. Today, we’re excited to announce Confluent Platform (CP) 8.3.0, built on Apache Kafka 4.3.0, reinforcing our core capabilities as a data streaming platform.