Stream Without Limit
Build systems that compute over data as it's created. Apache Flink powers ultra-low-latency, high-throughput pipelines that can easily handle both unbounded streams and bounded (batch) data sets, so your insights keep flowing.
State at Scale
Flink's strong state management and exactly-once semantics ensure your data processing is reliable, accurate, and consistent - even in fault conditions. From event-time processing through late or out-of-order data, your system stays correct.
One Engine, Dual Power
Why do you need to choose stream or batch? Flink does both. One architecture and API cover real-time streams and batch processing, optimized under the hood for whatever data you provide - without requiring additional tooling.
Recover Fast, Stay Resilient
Ingrained checkpointing and savepoints let Flink programs recover gracefully from crashes - or suspend and resume right where they left off. No data loss, no getting your bearings.
Connect Everywhere
Flink integrates seamlessly with Kafka, Cassandra, HDFS, S3, Elasticsearch, or more. Pipe things together from here to there.
Future-Ready with Community Momentum
Flink is open-source and backed by an active community and the Apache Software Foundation. With over a decade of live development, it's been battle-tested and remains in constant development.
Industry-Proven Reliability
Benchmarks expose Flink as a leader in fault recovery and stability among streaming frameworks. Flink is easily resilient to disorder, ensuring your data pipeline is rock-solid.
1. What is Apache Flink used for?
Apache Flink is a distributed stream processing platform for analyzing and responding to data in real time. It's used for applications like fraud detection, anomaly detection, real-time analytics, personalized recommendations, and monitoring systems where low latency and accuracy matter.
2. Is Flink limited to streaming data?
No. Flink is best known for real-time stream processing, but it also supports batch workloads with the same APIs and runtime. This one-framework approach lets you work with bounded datasets and unbounded streams without switching frameworks.
3. How does Flink provide reliability?
Flink provides exactly-once semantics through checkpointing and savepoints, so your data remains correct and consistent even when the system fails. It can resume to its exact state when it was interrupted.
4. Can Flink scale to very large workloads?
Yes. Flink is designed to run across clusters and cloud environments and scale out to enable extremely high throughput and massive state sizes. Every day, Flink-using enterprises handle billions of events.
5. What does Flink integrate with?
Flink integrates with many large data sources and sinks through connectors, including Apache Kafka, Cassandra, HDFS, S3, Elasticsearch, and JDBC databases. It allows for straightforward embedding into existing data infrastructure.
6. Is Apache Flink cloud-native?
Yes. Flink can be run on bare metal, VMs, Kubernetes, or fully managed cloud providers. Its elastic scalability and distributed architecture make it perfect for modern hybrid and cloud-native environments.