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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.

Most multi-entity businesses do not choose their accounting systems. They inherit them. A new acquisition brings Xero. The legacy entity has been on Dynamics for eight years. The Czech subsidiary runs on ABRA Flexi and nobody is going near it. Your US portco uses QuickBooks, your UK one uses Sage Intacct, and the newest deal came with a NetSuite instance that the previous owner had half-implemented. You are not looking at a technology problem. You are looking at the reality of how groups grow.

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.

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.

When was the last time you made a major business decision and were completely certain the data behind it was accurate, complete, and trusted? For most organizations, that certainty is less common than it should be. Qlik and Amazon Quick together solve one of the biggest obstacles to AI adoption: knowing whether you can trust the output.

When the Open Semantic Interchange (OSI) initiative launched last year, it set out to solve a problem every data leader recognizes: the same business metric gets defined a dozen different ways across a company's BI tools, warehouses, and now, AI agents. "Monthly active users" in the CRM rarely matches "monthly active users" in the warehouse, and every new AI copilot added to the stack makes the gap more visible, not less. That initiative has just taken its most consequential step yet.