The Fastest Way to Build
Ingest millions of events per second. Join and analyze live data streams with historical tables continuously. Serve ad-hoc queries in real-time. All using Postgres-style SQL.
Download open-source RisingWaveTrusted by 1,000+ Data-Driven Organizations
to harness continuous insights from both live and historical data.
What is RisingWave
Purpose-Built for Real-Time Streaming Data
Use Cases
Build Real-Time Pipelines and
Applications Faster Than Ever
Continuously monitor real-time event streams to detect anomalies or breaches within sub-seconds, and trigger instant alerts to any notification service.
Transform real-time events into AI features. Store, serve, and share features within a unified system, and inspect historical versions of the features at any previous time point.
Extract real-time insights from event streams and handle millions of concurrent queries with single-digit millisecond latency. Securely store historical data in low-cost tiered storage.
Develop scalable real-time data pipelines using SQL. Seamlessly ingest data from sources, leveraging efficient multi-way joins, automatic schema evolution, and out-of-order processing capabilities.
How RisingWave Can Help Your Business
COST EFFICIENCY
Engineered With
Cloud-Native Architecture
Start Simple, Go Advanced
RisingWave is wire-compatible with PostgreSQL, significantly lowering the barrier to mastering stream processing.
Process event streams with advanced features like watermarking, time windowing, and temporal filtering without compromising consistency.
Express complex logic in any language you prefer, such as Python, Java, and more. Run functions on servers or embed them with WebAssembly.
Automatically detect schema changes from upstream systems and propagate them to corresponding destinations without human intervention.
Query historical data as it existed in the past. Take snapshots at desired frequencies or any specific time point.
Next-Level Experience for Event Stream Processing
Stream Processors | OLAP Databases | RisingWave | |
---|---|---|---|
Continuous Ingestion | |||
Continuous Transformation | |||
Continuous Delivery | |||
Data persistence | |||
Query Serving |
We’ve Got Lots of Helpful Tips
and Resources for You
In this article, we'll show you how to set up a continuous data pipeline that seamlessly captures changes from your Postgres database using Change Data Capture (CDC) and streams them to Apache Iceberg.
By combining platforms like EMQX for industrial data streaming and RisingWave for real-time analytics, manufacturers can tap into machine-generated data as it happens, enabling predictive maintenance, reduced downtime, and improved efficiency. This integrated approach allows industries to respond swiftly to equipment failures, optimize production, and make data-driven decisions that boost overall equipment effectiveness (OEE) and operational agility.
In this article, we’ve demonstrated how to build a core fraud detection system using RisingWave. With minimal setup, you can easily integrate these components into your existing technical stack and have a functional fraud detection solution up and running.
Install RisingWave for macOS and Linux
See the quick start guide for next steps or other ways to run RisingWave.
curl -L https://risingwave.com/sh | sh