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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.

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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, with Apache Spark and StarRocks 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 updatable 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 🌊

Use Cases​

The following is a list of (but not limited to) use-cases that Fluss shines ✨:

  • 📊 Optimized Real-time analytics
  • 🔧 Feature Stores
  • 📈 Real-time Dashboards
  • 🧍 Real-time Customer 360
  • 📡 Real-time IoT Pipelines
  • 🚓 Real-time Fraud Detection
  • 🚨 Real-time Alerting Systems
  • 💫 Real-time ETL/Data Warehouses
  • 🌐 Real-time Geolocation Services
  • 🚚 Real-time Shipment Update Tracking

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.