RisingWave: An Open‑Source Stream‑Processing and Management Platform

Summary

🌊 Ride the Wave of Streaming Data. Docs | Benchmarks | Demos RisingWave is a stream processing and management platform designed to offer the simplest and most cost-effective way to process, analyze, and manage real-time event data — with built-in support for the Apache Iceberg™ open table format. It provides both a Postgres-compatible SQL interface and a DataFrame-style Python interface. RisingWave can ingest millions of events per second, continuously join and analyze live streams with historical data, serve ad-hoc queries at low latency, and persist fresh, consistent results to Apache Iceberg™ or any other downstream system. Try it out in 60 seconds Install RisingWave standalone mode: curl -L https://risingwave.com/sh | sh To learn about other installation options, such as using a Docker image, see Quick Start. Stream, Store, and Query — All in One RisingWave delivers a full end-to-end streaming data platform — combining real-time processing with built-in storage and open-format persistence. It supports: Ingestion: Ingest millions of events per second from streaming and batch sources. Ingest millions of events per second from streaming and batch sources. Stream processing: Perform real-time incremental processing to join and analyze live data with historical tables. Perform real-time incremental processing to join and analyze live data with historical tables. Delivery: Deliver fresh, consistent results to data lakes (e.g., Apache Iceberg™) or any destination. What sets RisingWave apart is its integrated storage engine: Online serving: Row-based storage optimized for point and range queries with single-digit millisecond latency. Row-based storage optimized for point and range queries with single-digit millisecond latency. Offline persistence: Built-in Apache Iceberg™ integration for low-cost, durable storage with open access for external query engines. With RisingWave, real-time data isn’t just processed — it’s stored, queried, and shared across your entire stack. ...