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
From Monolith to Microservices with Sam Newman
Exploring Event Streaming Use Cases with µKanren ft. Tim Baldridge
Introducing JSON and Protobuf Support ft. David Araujo and Tushar Thole
Scaling Apache Kafka in Retail with Microservices ft. Matt Simpson from Boden
Connecting Snowflake and Apache Kafka ft. Isaac Kunen
AMA with Tim Berglund | Streaming Audio Special
Kubernetes Meets Apache Kafka ft. Kelsey Hightower
Apache Kafka Fundamentals: The Concept of Streams and Tables ft. Michael Noll
IoT Integration and Real-Time Data Correlation with Kafka Connect and Kafka Streams ft. Kai Waehner
Confluent Platform 5.5 | What's New in This Release + Updates
Making Abstract Algebra Count in the World of Event Streaming ft. Sam Ritchie
Apache Kafka 2.5 – Overview of Latest Features, Updates, and KIPs
Streaming Data Integration – Where Development Meets Deployment ft. James Urquhart
How to Run Kafka Streams on Kubernetes ft. Viktor Gamov
Cloud Marketplace Considerations with Dan Rosanova
Explore, Expand, and Extract with 3X Thinking ft. Kent Beck
Ask Confluent #17: The “What is Apache Kafka?” Episode ft. Tim Berglund
Domain-Driven Design and Apache Kafka with Paul Rayner
Machine Learning with TensorFlow and Apache Kafka ft. Chris Mattmann
Distributed Systems Engineering with Apache Kafka ft. Gwen Shapira
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