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
- Flink Community Update - April'20
- While things slow down around us, the Apache Flink community is privileged to remain as active as ever. This blogpost combs through the past few months to give you an update on the state of things in Flink — from core releases to Stateful Functions; from some good old community stats to a new development blog.
- Flink as Unified Engine for Modern Data Warehousing: Production-Ready Hive Integration
In this blog post, you will learn our motivation behind the Flink-Hive integration, and how Flink 1.10 can help modernize your data warehouse.
- Advanced Flink Application Patterns Vol.2: Dynamic Updates of Application Logic
- In this series of blog posts you will learn about powerful Flink patterns for building streaming applications.
- Apache Beam: How Beam Runs on Top of Flink
- This blog post discusses the reasons to use Flink together with Beam for your stream processing needs and takes a closer look at how Flink works with Beam under the hood.
- No Java Required: Configuring Sources and Sinks in SQL
- This post discusses the efforts of the Flink community as they relate to end to end applications with SQL in Apache Flink.