Kafka has become a go-to platform for organizations to move their data between infrastructure and tools to analyze and audit their networks for log streaming. While it has many advantages, it can also present several challenges, such as scalability and bottlenecks that can limit system effectiveness.
The Apache Kafka open source platform is particularly popular in the context of big data applications that require the processing of many data streams. Version 2.4 of the stream processing software has now been released – consumers can now fetch from closest replica and there is a new Java authorizer API.
While real-time search engine Elasticsearch is known for its scalability, LinkedIn’s Kafka is a reliably fast messaging system. Mariam Hakobyan shows us how the two work together as a fast and performance-optimised duo.
Theres a new project in the Apache Incubator, as LinkedIn pushes their stream processing framework to the open source foundation.
We speak to Principle Software Engineer at LinkedIn, about Kafka’s proposed move.
PLUS, new Clojure micro-framework and Hibernate Core 4.0.0 Beta2.
PLUS, LinkedIn open source Kafka and Lift moves beyond Scala!
