Apache Flink® — Stateful Computations over Data Streams



All streaming use cases
  • Event-driven Applications
  • Stream & Batch Analytics
  • Data Pipelines & ETL
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Guaranteed correctness
  • Exactly-once state consistency
  • Event-time processing
  • Sophisticated late data handling
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Layered APIs
  • SQL on Stream & Batch Data
  • DataStream API & DataSet API
  • ProcessFunction (Time & State)
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Operational Focus
  • Flexible deployment
  • High-availability setup
  • Savepoints
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Scales to any use case
  • Scale-out architecture
  • Support for very large state
  • Incremental checkpointing
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Excellent Performance
  • Low latency
  • High throughput
  • In-Memory computing
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Apache Beam: How Beam Runs on Top of Flink
This blog post discusses the reasons to use Flink together with Beam for your stream processing needs and takes a closer look at how Flink works with Beam under the hood.
No Java Required: Configuring Sources and Sinks in SQL
This post discusses the efforts of the Flink community as they relate to end to end applications with SQL in Apache Flink.
Apache Flink 1.10.0 Release Announcement

The Apache Flink community is excited to hit the double digits and announce the release of Flink 1.10.0! As a result of the biggest community effort to date, with over 1.2k issues implemented and more than 200 contributors, this release introduces significant improvements to the overall performance and stability of Flink jobs, a preview of native Kubernetes integration and great advances in Python support (PyFlink).

A Guide for Unit Testing in Apache Flink
This post provides a detailed guide for unit testing of Apache Flink applications.
Apache Flink 1.9.2 Released

The Apache Flink community released the second bugfix version of the Apache Flink 1.9 series.