Why Apache Flink®?

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      Stream processing

      Implement robust continuous applications that never stop and get immediate insights from your data.

       
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      Low latency

      Write latency-critical applications with millisecond responses.

       
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      High throughput

      Flink can handle millions of events per second in moderate-sized and scale to 1000s of nodes.

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      Fault tolerant

      Flink is highly available and fault tolerant; the results of your computation will be correct after failures.

       
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      Correct stream handling

      Flink embraces the notion of event time, guaranteeing that out of order events are handled correctly.

       
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      Batch

      Flink has full batch processing capabilities by treating batch as a special case of streaming.

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      APIs and libraries

      Choose Flink’s own DataStream, DataSet, SQL, CEP, Gelly, and FlinkML APIs, or use compatibility layers for MapReduce, Storm, Cascading, and Beam.

       
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      Rich ecosystem

      Batteries included; Flink comes with support for Kafka, HDFS, HBase, Kinesis, S3, RabbitMQ, Elastic, Cassandra and YARN.

       
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      Community

      With over 190 contributors and an impressive list of users, Flink is one of the most active Big Data projects in the ASF.