Confluent Platform 7.0 has launched and includes Apache Kafka® 3.0, plus new features introduced by KIP-630: Kafka Raft Snapshot, KIP-745: Connect API to restart connector and task, and KIP-695: Further improve Kafka Streams timestamp synchronization. Reporting from Dubai, Tim Berglund (Senior Director, Developer Advocacy, Confluent) provides a summary of new features, updates, and improvements to the 7.0 release, including the ability to create a real-time bridge from on-premises environments to the cloud with Cluster Linking.
Cluster Linking allows you to create a single cluster link between multiple environments from Confluent Platform to Confluent Cloud, which is available on public clouds like AWS, Google Cloud, and Microsoft Azure, removing the need for numerous point-to-point connections. Consumers reading from a topic in one environment can read from the same topic in a different environment without risks of reprocessing or missing critical messages. This provides operators the flexibility to make changes to topic replication smoothly and byte for byte without data loss. Additionally, Cluster Linking eliminates any need to deploy MirrorMaker2 for replication management while ensuring offsets are preserved.
Furthermore, the release of Confluent for Kubernetes 2.2 allows you to build your own private cloud in Kafka. It completes the declarative API by adding cloud-native management of connectors, schemas, and cluster links to reduce the operational burden and manual processes so that you can instead focus on high-level declarations. Confluent for Kubernetes 2.2 also enhances elastic scaling through the Shrink API.
Following ZooKeeper’s removal in Apache Kafka 3.0, Confluent Platform 7.0 introduces KRaft in preview to make it easier to monitor and scale Kafka clusters to millions of partitions. There are also several ksqlDB enhancements in this release, including foreign-key table joins and the support of new data types—DATE and TIME— to account for time values that aren’t TIMESTAMP. This results in consistent data ingestion from the source without having to convert data types.
EPISODE LINKS
Data-Driven Digitalization with Apache Kafka in the Food Industry at BAADER
Chaos Engineering with Apache Kafka and Gremlin
Boosting Security for Apache Kafka with Confluent Cloud Private Link ft. Dan LaMotte
Confluent Platform 6.2 | What’s New in This Release + Updates
Adopting OpenTelemetry in Confluent and Beyond ft. Xavier Léauté
Running Apache Kafka Efficiently on the Cloud ft. Adithya Chandra
Engaging Database Partials with Apache Kafka for Distributed System Consistency ft. Pat Helland
The Truth About ZooKeeper Removal and the KIP-500 Release in Apache Kafka ft. Jason Gustafson and Colin McCabe
Resilient Edge Infrastructure for IoT Using Apache Kafka ft. Kai Waehner
Data Management and Digital Transformation with Apache Kafka at Van Oord
Powering Microservices Using Apache Kafka on Node.js with KafkaJS at Klarna ft. Tommy Brunn
Apache Kafka 2.8 - ZooKeeper Removal Update (KIP-500) and Overview of Latest Features
Connecting Azure Cosmos DB with Apache Kafka - Better Together ft. Ryan CrawCour
Automated Cluster Operations in the Cloud ft. Rashmi Prabhu
Resurrecting In-Sync Replicas with Automatic Observer Promotion ft. Anna McDonald
Building Real-Time Data Pipelines with Microsoft Azure, Databricks, and Confluent
Smooth Scaling and Uninterrupted Processing with Apache Kafka ft. Sophie Blee-Goldman
Event-Driven Architecture - Common Mistakes and Valuable Lessons ft. Simon Aubury
The Human Side of Apache Kafka and Microservices ft. SPOUD
Gamified Fitness at Synthesis Software Technologies Using Apache Kafka and IoT
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