Apache Flink® is an open source stateful stream processing framework to build robust real-time data applications at scale: it enables fast, accurate and fault tolerant handling of massive streams of events. Apache Flink also supports batch processing as a special case of streaming.
Ververica engineers wrote the first lines of what would later become Apache Flink in 2010 and, as members of the open source community, are dedicated to pushing Apache Flink further to this day.
Ververica Platform has been built for companies that are just getting started with stateful stream processing, as well as experienced users looking to operate their streaming applications more effectively. If you’re new to stream processing and would like to learn more, we recommend starting with our downloadable report “Stream Processing for Real-Time Businesses, Powered by Apache Flink”.
Working with the company founded by the original creators of Apache Flink ensures development teams to have access to three years of 24/7 support by leading experts for every minor release.
Ververica Platform comes with three years of off-cycle bug fixes and security patches for every minor release, ensuring you get all the support to run confidently in production and recover fast in disaster scenarios.
The Flink distribution in Ververica Platform was primarily designed and built for Kubernetes. It includes a set of hardened, optional components to promptly get up and running on Kubernetes, in the cloud and on-premise.
Apache Flink provides true event-at-a-time stream processing, enabling 24/7, continuous applications for immediate insights and actions on your data.
Apache Flink processes millions of events per second in real-time and powers stream processing applications on 1000s of nodes in production.
Apache Flink provides highly available and fault tolerant stream processing; Flink supports exactly once semantics even in the event of failure.
Apache Flink's savepoints make it possible for a user to fix issues, reprocess data, update code, and manage upgrades easily and with data consistency.
Apache Flink embraces the notion of event time in stream processing, guaranteeing that out of order events are handled correctly and that results are accurate.
Apache Flink's application state is rescalable, making it possible to add more resources while maintaining exactly once semantics in the application.
Apache Flink offers a streaming SQL API, making it accessible for business and non-technical users to harness the power of stream processing.
Apache Flink's complex event processing (CEP) library makes it possible to detect and respond to mission-critical business events in real-time.
Apache Flink's stream processing APIs make it easy to model complex, real-world business problems by exposing key building blocks to developers.
Apache Flink has full batch processing capabilities, where batch is a special case of stream processing. Flink offers a unified data processing framework.
Apache Flink supports the stream processing ecosystem, including Kafka, HDFS, Kinesis, Cassandra, DC/OS, Mesos, Docker, Kubernetes, and YARN.
Apache Flink has 330+ contributors and a long list of production users; it is one of the most active stream processing and big data projects in ASF.
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