Skip to main content

What is Fluss?

Fluss is a streaming storage built for real-time analytics which can serve as the real-time data layer for Lakehouse architectures.

arch

It bridges the gap between streaming data and the data Lakehouse by enabling low-latency, high-throughput data ingestion and processing while seamlessly integrating with popular compute engines like Apache Flink, while Apache Spark, and StarRocks are coming soon.

Fluss supports streaming reads and writes with sub-second latency and stores data in a columnar format, enhancing query performance and reducing storage costs. It offers flexible table types, including append-only Log Tables and updateable PrimaryKey Tables, to accommodate diverse real-time analytics and processing needs.

With built-in replication for fault tolerance, horizontal scalability, and advanced features like high-QPS lookup joins and bulk read/write operations, Fluss is ideal for powering real-time analytics, AI/ML pipelines, and streaming data warehouses.

Fluss (German: river, pronounced /flus/) enables streaming data continuously converging, distributing and flowing into lakes, like a river 🌊

Where to go Next?​

  • QuickStart: Get started with Fluss in minutes.
  • Architecture: Learn about Fluss's architecture.
  • Table Design: Explore Fluss's table types, partitions and buckets.
  • Lakehouse: Integrate Fluss with your Lakehouse to bring low-latency data to your Lakehouse analytics.
  • Development: Set up your development environment and contribute to the community.