Kafka in the Cloud: Why it’s 10x better with Confluent | Find out more
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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Handling the Producer Request: Kafka Producer and Consumer Internals, Part 2
In this post, the second in the Kafka Producer and Consumer Internals Series, we follow our brave hero—a well-formed produce request—which is on its way to be processed by the broker and have its data stored on the cluster.
How Producers Work: Kafka Producer and Consumer Internals, Part 1
The beauty of Kafka as a technology is that it can do a lot with little effort on your part. In effect, it’s a black box. But what if you need to see into the black box to debug something? This post shows what the producer does behind the scenes to help prepare your raw event data for the broker.