Apache Flink®

Stateful Computations over Data Streams

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

Flink Capabilities

Exactly-once state consistency

Event-time processing

Sophisticated late data handling

SQL on Stream & Batch Data

DataStream API

ProcessFunction (Time & State)

Flexible deployment

High-availability setup

Savepoints

Scale-out architecture

Support for very large state

Incremental Checkpoints

Low latency

High throughput

In-Memory computing

Use Cases

An event-driven application is a stateful application that ingests events from one or more event streams and reacts to incoming events by triggering computations, state updates, or external actions.

Analytical jobs extract information and insight from raw data. Apache Flink supports traditional batch queries on bounded data sets and real-time, continuous queries from unbounded, live data streams.

Extract-transform-load (ETL) is a common approach to convert and move data between storage systems.

Recent Flink blogs

June 3, 2025 - Gabor Somogyi.

The Apache Flink community is excited to announce the release of Flink Kubernetes Operator 1.12.0! The version brings a number of important fixes and improvements to both core and autoscaler modules. …

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May 16, 2025 - Yanquan Lv.

The Apache Flink Community is excited to announce the release of Flink CDC 3.4.0! This release introduces a new pipeline Connector for Apache Iceberg, and provides support for batch execution mode, …

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April 30, 2025 - Ferenc Csaky.

We are pleased to announce the revival of a connector that makes it possible for Flink to interact with Apache Kudu. The original connector existed as part of the Apache Bahir project, which was moved …

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