Apache Flink® — Stateful Computations over Data Streams
All streaming use cases
- Event-driven Applications
- Stream & Batch Analytics
- Data Pipelines & ETL
Guaranteed correctness
- Exactly-once state consistency
- Event-time processing
- Sophisticated late data handling
Layered APIs
- SQL on Stream & Batch Data
- DataStream API & DataSet API
- ProcessFunction (Time & State)
Operational Focus
- Flexible deployment
- High-availability setup
- Savepoints
Scales to any use case
- Scale-out architecture
- Support for very large state
- Incremental checkpointing
Excellent Performance
- Low latency
- High throughput
- In-Memory computing
- Advanced Flink Application Patterns Vol.1: Case Study of a Fraud Detection System
- In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications.
- Apache Flink 1.8.3 Released
The Apache Flink community released the third bugfix version of the Apache Flink 1.8 series.
- Running Apache Flink on Kubernetes with KUDO
- A common use case for Apache Flink is streaming data analytics together with Apache Kafka, which provides a pub/sub model and durability for data streams. In this post, we demonstrate how to orchestrate Flink and Kafka with KUDO.
- How to query Pulsar Streams using Apache Flink
- This blog post discusses the new developments and integrations between Apache Flink and Apache Pulsar and showcases how you can leverage Pulsar’s built-in schema to query Pulsar streams in real time using Apache Flink.
- Apache Flink 1.9.1 Released
The Apache Flink community released the first bugfix version of the Apache Flink 1.9 series.