Best of Apache Kafka — February 2026

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   Article

   BigData Boutique blog·14w

   The KFC Architecture Blueprint: Kafka, Flink, and ClickHouse

   The KFC Architecture Blueprint combines Kafka, Flink, and ClickHouse into an end-to-end real-time data processing stack. Kafka handles event ingestion and decoupling, Flink manages stateful stream processing (windowed joins, sessionization, exactly-once semantics), and ClickHouse delivers sub-second analytical queries at scale. The pattern suits use cases like fraud detection, clickstream analytics, IoT telemetry, and financial market data. However, the full stack is often overkill — many workloads can skip Flink entirely by using ClickHouse's Kafka Table Engine with materialized views. The practical guidance: start with Kafka feeding directly into ClickHouse, and only add Flink when you genuinely need stateful joins, complex windowing, or exactly-once delivery with side effects.

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2. 2
   Article

   Confluent Blog·13w

   Apache Kafka 4.2.0 Released: Share Groups, Streams & More

   Apache Kafka 4.2.0 is now available, bringing several major improvements. Share Groups (Kafka Queues) are now production-ready, featuring a new RENEW acknowledgement type for extended processing, adaptive batching for share coordinators, configurable fetch record limits, and comprehensive lag metrics. Kafka Streams gains GA status for its server-side rebalance protocol, dead letter queue support in exception handlers, anchored wall-clock punctuation for deterministic scheduling, and explicit control over leave-group behavior on shutdown. Observability is improved with standardized CLI arguments, corrected metric naming following the kafka.COMPONENT convention, and new idle ratio metrics for controllers and MetadataLoader. Security enhancements include an allowlist connector client configuration override policy and thread-safety fixes to RecordHeader. Additional changes cover external schema support in JsonConverter, dynamic remote log manager thread pool configuration, adaptive batching in group coordinators, and rack ID exposure in the Admin API.

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3. 3
   Article

   BigData Boutique blog·13w

   Kafka MirrorMaker 2: Deployment, Gotchas, and Disaster Recovery Failback Playbook

   A practical guide to deploying Kafka MirrorMaker 2 (MM2) for cluster replication, covering deployment topology, connector configuration, and production gotchas. Key decisions include deploying MM2 alongside the target cluster, choosing between DefaultReplicationPolicy and IdentityReplicationPolicy before going live, and tuning client parameters for high-throughput workloads. Common pitfalls include config drift (sync.topic.configs.enabled is unreliable), and topic recreation on the source causing scrambled offsets that require manual connector reset. The post closes with a detailed failback playbook: validate primary cluster health, establish temporary reverse replication from DR to primary, move consumers before producers, drain replication lag to zero before producer cutover, and always rehearse the procedure in non-production first.

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