Provides a serverless stream processing platform and an open-source distributed streaming database that enable real-time ingestion, transformation, and analysis of high-velocity data streams alongside historical data. This unified solution supports use cases like continuous analytics, real-time ETL, and feature engineering, delivering sub-second query performance and seamless integration with tools like Kafka, PostgreSQL, and Snowflake.
Funding
$46M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional stream processing systems and OLAP databases often operate in silos, requiring complex data pipelines to ingest, transform, and analyze both real-time and historical data. This separation leads to increased latency, higher infrastructure costs, and difficulties in maintaining data consistency across different systems.
Solution
RisingWave provides a unified platform for stream processing and data warehousing, enabling real-time ingestion, transformation, and analysis of streaming data alongside historical data using standard SQL. It combines the capabilities of stream processors and OLAP databases into a single, scalable platform, eliminating the need for separate systems and complex ETL processes. RisingWave allows users to build real-time pipelines and event-driven applications faster than ever, extracting fresh and consistent insights from real-time event streams, database CDC, and time series data within sub-seconds.
Target Audience
RisingWave is designed for data engineers, data scientists, and analysts who need to build real-time data pipelines, continuous analytics services, and event-driven applications.
Features
- Wire-compatible with PostgreSQL, lowering the barrier to mastering stream processing
- Decoupled compute and storage architecture for cost efficiency
- Efficient streaming joins, transparent dynamic scaling, instant failure recovery, speedy bootstrapping and backfilling, and built-in data serving
- Advanced features like watermarking, time windowing, and temporal filtering without compromising consistency
- Support for user-defined functions in languages like Python and Java, which can be run on servers or embedded with WebAssembly
- Automatic schema evolution to detect and propagate schema changes from upstream systems without human intervention
- Time travel capabilities to query historical data as it existed in the past
- Direct integration with data systems like Apache Kafka, PostgreSQL, MySQL, ClickHouse, Snowflake, Apache Iceberg, and more