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Log Compaction | Highlights in the Apache Kafka and Stream Processing Community | June 2016
After months of testing efforts and seven voting rounds, Apache Kafka 0.10.0 and the corresponding Confluent Platform 3.0 have been finally released, cheers!
- Kafka 0.10.0 is the newest major release of Apache Kafka, which includes more than 400 JIRAs and 13 KIPs, including Kafka Streams — a simple solution for stream processing, Message Timestamps — an additional field inside the message that enables log retention and rolling as well as stream processing based on event-time, Rack Awareness — functionality to assign replicas to brokers so they can span multiple racks or availability zones, and many other new features.
- Confluent Platform 3.0 introduces the first commercial product through the Confluent Platform offering — Control Center, for comprehensive management and monitoring of Apache Kafka. Read this page to learn more.
- Videos and slides from Kafka Summit 2016 have been made available!
- Ismael Juma joins Apache Kafka community as the 14th committer, welcome Ismael!
- Kafka Connect contributions have been growing rapidly over the past month! Checkout this new hub for available certified connectors.
- Thanks to Yelp for hosting the Kafka Meetup this month! Enrico Canzonieri and Usman Masood gave great presentations on operating Kafka at scale and executing continuous SQL queries on top of Kafka, respectively.
- Ewen Cheslack-Postava from Confluent talked about how to stream data integration with Kafka Connect at Apache Big Data 2016.
- Martin Kleppmann compares using event streams and Kafka for keeping data in sync across heterogeneous systems with distributed transactions.
- Parth Brahmbhatt from Hortonworks talked about how to secure Kafka clusters.
- If you want to learn more about applying Kafka for real-time data analysis, read this article for a production use case in online anomaly detection, and also this talk about using Kafka and Samza for spam detection, and also this blog post about using Kafka and Flink to migrate your batch jobs to streaming.
- Why Apache Beam for stream processing? Checkout Google and data Artisan’s perspectives.
- Exciting news from Apache Calcite to extend SQL and relational algebra in order to support streaming queries.
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Building Streaming Data Pipelines, Part 1: Data Exploration With Tableflow
This blog post demonstrates using Tableflow to easily transform Kafka topics into queryable Iceberg tables. It uses UK Environment Agency sensor data as a data source, and shows how to use Tableflow with standard SQL to explore and understand the data.
Guide to Consumer Offsets: Manual Control, Challenges, and the Innovations of KIP-1094
The guide covers Kafka consumer offsets, the challenges with manual control, and the improvements introduced by KIP-1094. Key enhancements include tracking the next offset and leader epoch accurately. This ensures consistent data processing, better reliability, and performance.