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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Advanced Flink Application Patterns Vol.1: Case Study of a Fraud Detection System
In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications.
Apache Flink 1.8.3 Released

The Apache Flink community released the third bugfix version of the Apache Flink 1.8 series.

Running Apache Flink on Kubernetes with KUDO
A common use case for Apache Flink is streaming data analytics together with Apache Kafka, which provides a pub/sub model and durability for data streams. In this post, we demonstrate how to orchestrate Flink and Kafka with KUDO.
How to query Pulsar Streams using Apache Flink
This blog post discusses the new developments and integrations between Apache Flink and Apache Pulsar and showcases how you can leverage Pulsar’s built-in schema to query Pulsar streams in real time using Apache Flink.
Apache Flink 1.9.1 Released

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