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
Ask Confluent #18: The Toughest Questions ft. Anna McDonald
Joining Forces with Spring Boot, Apache Kafka, and Kotlin ft. Josh Long
Building an Apache Kafka Center of Excellence Within Your Organization ft. Neil Buesing
Creating Your Own Kafka Improvement Proposal (KIP) as a Confluent Intern ft. Leah Thomas
Confluent Platform 6.0 | What's New in This Release + Updates
Using Event Modeling to Architect Event-Driven Information Systems ft. Bobby Calderwood
Using Apache Kafka as the Event-Driven System for 1,500 Microservices at Wix ft. Natan Silnitsky
Top 6 Things to Know About Apache Kafka ft. Gwen Shapira
5 Years of Event Streaming and Counting ft. Gwen Shapira, Ben Stopford, and Michael Noll
Championing Serverless Eventing at Google Cloud ft. Jay Smith
Disaster Recovery with Multi-Region Clusters in Confluent Platform ft. Anna McDonald and Mitch Henderson
Developer Advocacy (and Kafka Summit) in the Pandemic Era
Apache Kafka 2.6 - Overview of Latest Features, Updates, and KIPs
Testing ksqlDB Applications ft. Viktor Gamov
How to Measure the Business Value of Confluent Cloud ft. Lyndon Hedderly
Modernizing Inventory Management Technology ft. Sina Sojoodi and Rohit Kelapure
Fault Tolerance and High Availability in Kafka Streams and ksqlDB ft. Matthias J. Sax
Benchmarking Apache Kafka Latency at the 99th Percentile ft. Anna Povzner
Open Source Workflow Automation with Apache Kafka ft. Bernd Ruecker
Growing the Event Streaming Community During COVID-19 ft. Ale Murray
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