14 Apr 2016 by Aljoscha Krettek (@aljoscha)
We are happy to announce that the call for submissions for Flink Forward 2016 is now open! The conference will take place September 12-14, 2016 in Berlin, Germany, bringing together the open source stream processing community. Most Apache Flink committers will attend the conference, making it the ideal venue to learn more about the project and its roadmap and connect with the community.
06 Apr 2016 by Till Rohrmann (@stsffap)
In this blog post, we introduce Flink's new CEP library that allows you to do pattern matching on event streams. Through the example of monitoring a data center and generating alerts, we showcase the library's ease of use and its intuitive Pattern API.
06 Apr 2016
Today, the Flink community released Flink version 1.0.1, the first bugfix release of the 1.0 series.
08 Mar 2016
The Apache Flink community is pleased to announce the availability of the 1.0.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on improving the experience of writing and executing data stream processing pipelines in production.
11 Feb 2016
Today, the Flink community released Flink version 0.10.2, the second bugfix release of the 0.10 series.
18 Dec 2015 by Robert Metzger (@rmetzger_)
With 2015 ending, we thought that this would be good time to reflect on the amazing work done by the Flink community over this past year, and how much this community has grown.
11 Dec 2015 by Matthias J. Sax (@MatthiasJSax)
In this blog post, we describe Flink's compatibility package for Apache Storm that allows to embed Spouts (sources) and Bolts (operators) in a regular Flink streaming job. Furthermore, the compatibility package provides a Storm compatible API in order to execute whole Storm topologies with (almost) no code adaption.
04 Dec 2015 by Fabian Hueske (@fhueske)
The data analysis space is witnessing an evolution from batch to stream processing for many use cases. Although batch can be handled as a special case of stream processing, analyzing never-ending streaming data often requires a shift in the mindset and comes with its own terminology (for example, “windowing” and “at-least-once”/”exactly-once” processing). This shift and the new terminology can be quite confusing for people being new to the space of stream processing. Apache Flink is a production-ready stream processor with an easy-to-use yet very expressive API to define advanced stream analysis programs. Flink's API features very flexible window definitions on data streams which let it stand out among other open source stream processors.
In this blog post, we discuss the concept of windows for stream processing, present Flink's built-in windows, and explain its support for custom windowing semantics.
27 Nov 2015
Today, the Flink community released the first bugfix release of the 0.10 series of Flink.
16 Nov 2015
The Apache Flink community is pleased to announce the availability of the 0.10.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on data stream processing and operational features. About 80 contributors provided bug fixes, improvements, and new features such that in total more than 400 JIRA issues could be resolved.