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42---
title: Introduction
sidebar_position: 1
slug: /
---
# 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.

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 ๐
## Use Cases
The following is a list of (but not limited to) use-cases that Fluss shines โจ:
* **๐ Optimized Real-time analytics**
* **๐ง Feature Stores**
* **๐ Real-time Dashboards**
* **๐ง Real-time Customer 360**
* **๐ก Real-time IoT Pipelines**
* **๐ Real-time Fraud Detection**
* **๐จ Real-time Alerting Systems**
* **๐ซ Real-time ETL/Data Warehouses**
* **๐ Real-time Geolocation Services**
* **๐ Real-time Shipment Update Tracking**
## Where to go Next?
- [QuickStart](quickstart/flink.md): Get started with Fluss in minutes.
- [Architecture](concepts/architecture.md): Learn about Fluss's architecture.
- [Table Design](table-design/overview.md): Explore Fluss's table types, partitions and buckets.
- [Lakehouse](streaming-lakehouse/overview.md): Integrate Fluss with your Lakehouse to bring low-latency data to your Lakehouse analytics.
- [Development](/community/dev/ide-setup): Set up your development environment and contribute to the community.