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Apache Flink enables real-time streaming data processing and fast batch data processing. It is known for its scalability and fault tolerance, finding applications across various domains such as data analytics and real-time applications.
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Apache Flink is a distributed engine for stateful computations over unbounded and bounded data streams. It processes continuous events and traditional batch data through the same dataflow model.
Apache Flink has its roots in the Stratosphere research project, which began in 2009 at TU Berlin together with other Berlin and European research institutions. The project entered the Apache Incubator in 2014 and became an Apache Software Foundation Top-Level Project at the end of that year.
A Flink application forms a directed dataflow from sources through operators to sinks. Events move continuously through this pipeline; operators can retain distributed state to compute windows, patterns, or aggregations. Watermarks help interpret event time and late data. Checkpoints store consistent snapshots of the data stream and state so that processing can resume after a failure.
Sources, operators, and sinks form a distributed processing pipeline.
Operators retain information across multiple events for continuous computations.
Timestamps and progress markers arrange events by their domain time of occurrence.
Consistent snapshots enable recovery and defined processing guarantees.
The same model processes finite data sets and continuously arriving events.
Flink fits continuous data pipelines, real-time analytics, fraud detection, and event-driven applications with demanding time or state logic. Its value grows with high data rates and consistent recovery; state size, backpressure, checkpointing, and cluster operation still require operational expertise.
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