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
Real-Time Stream Processing with Kafka Streams ft. Bill Bejeck
Automating Infrastructure as Code with Apache Kafka and Confluent ft. Rosemary Wang
Getting Started with Spring for Apache Kafka ft. Viktor Gamov
Powering Event-Driven Architectures on Microsoft Azure with Confluent
Automating DevOps for Apache Kafka and Confluent ft. Pere Urbón-Bayes
Intro to Kafka Connect: Core Components and Architecture ft. Robin Moffatt
Designing a Cluster Rollout Management System for Apache Kafka ft. Twesha Modi
Apache Kafka 3.0 - Improving KRaft and an Overview of New Features
How to Build a Strong Developer Community with Global Engagement ft. Robin Moffatt and Ale Murray
What Is Data Mesh, and How Does it Work? ft. Zhamak Dehghani
Multi-Cluster Apache Kafka with Cluster Linking ft. Nikhil Bhatia
Using Apache Kafka and ksqlDB for Data Replication at Bolt
Placing Apache Kafka at the Heart of a Data Revolution at Saxo Bank
Advanced Stream Processing with ksqlDB ft. Michael Drogalis
Minimizing Software Speciation with ksqlDB and Kafka Streams ft. Mitch Seymour
Collecting Data with a Custom SIEM System Built on Apache Kafka and Kafka Connect ft. Vitalii Rudenskyi
Consistent, Complete Distributed Stream Processing ft. Guozhang Wang
Powering Real-Time Analytics with Apache Kafka and Rockset
Automated Event-Driven Architectures and Microservices with Apache Kafka and SmartBear
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