Apache Kafka

Planning an Apache Kafka® migration? Learn how to estimate migration expenses, reduce costs, and compare self-managed vs managed real-time data platforms with expert insight.

Explore the hidden costs of real-time streaming—compare infrastructure, ops, and ROI between Confluent Cloud and self-managed Apache Kafka®. Learn how auto-scaling and governance lower TCO.

Confluent, powered by Kafka, is the real-time backbone for agentic systems built with Google Cloud. It enables agents to access fresh data (MCP) and communicate seamlessly (A2A) via a decoupled architecture. This ensures scalability, resilience, and observability for complex, intelligent workflows.

AWS Lambda's Kafka Event Source Mapping now supports Confluent Schema Registry. This update simplifies building event-driven applications by eliminating the need for custom code to deserialize Avro/Protobuf data. The integration makes it easier and more efficient to leverage Confluent Cloud.

Confluent’s Cluster Linking enables fully managed, offset-preserving Kafka replication across clouds. It supports public and private networking, enabling use cases like disaster recovery, data sharing, and analytics across AWS, Azure, Google Cloud, and on-premises clusters.

Confluent Cloud now offers native Kafka Streams health monitoring to simplify troubleshooting. The new UI provides at-a-glance application state, performance ratios to pinpoint bottlenecks (code vs. cluster), and state store metrics.

Learn how to scale Kafka Streams applications to handle massive throughput with partitioning, scaling strategies, tuning, and monitoring.

Learn how to choose the right Apache Kafka® multi-cluster replication pattern and run an audit-ready disaster recovery and high availability program with lag SLOs, drills, and drift control.

Learn how to handle data transformation, schema evolution, and security in Kafka Connect with best practices for consistency, enrichment, and format conversions.

Learn best practices for validating your Apache Kafka® disaster recovery and high availability strategies, using techniques like chaos testing, monitoring, and documented recovery playbooks.

Learn best practices for running Kafka Connect in production—covering scaling, security, error handling, and monitoring to build resilient data integration pipelines.

Announcing the release of Apache Kafka 4.1

Confluent Tableflow unifies operational and analytical data by integrating Kafka with zero ETL, leveraging open table formats such as Iceberg and Delta Lake. It offers advantages over traditional zero ETL by enabling data reuse, schema decoupling, and better scalability, streamlining data sharing.

Learn how to build a custom Kafka connector, which is an essential skill for anyone working with Apache Kafka® in real-time data streaming environments with a wide variety of data sources and sinks.