RisingWave

Founded by Yingjun Wu in 2021

Process real-time event streams using SQL with sub-second data freshness

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use RisingWave to ingest, transform, and serve live data from event streams using PostgreSQL-compatible SQL. It handles real-time stream processing with under 100ms latency, materialized views that update continuously, and built-in storage so you don't need separate caches or databases. It connects to Apache Kafka, writes to Apache Iceberg tables for lakehouse analytics, supports exactly-once processing guarantees, and runs complex multi-stream joins and aggregations without external orchestration.

What RisingWave does

Write SQL materialized views that continuously process incoming event streamsConnect to Kafka topics as data sources using SQL CREATE SOURCE statementsSink processed streaming data into Apache Iceberg tables with a single SQL statementRun multi-way joins across multiple live data streams simultaneouslySet up change data capture from databases with exactly-once guaranteesQuery historical states of materialized views for auditing or backtestingScale compute and storage independently to manage cost during traffic spikesDefine watermarks and event-time windows for precise temporal aggregationsPostgreSQL wire-compatible SQL for defining sources, views, and sinksSub-100ms stream processing with exactly-once guaranteesBuilt-in row and columnar storage, no external database requiredManaged Apache Iceberg table maintenance with automatic compactionCascading materialized views that update incrementally in real timeCDC with exactly-once semantics including TOAST column supportDecoupled compute and storage for independent scaling and cost efficiencyTime travel queries to reconstruct historical states of materialized views

Pricing breakdown

Pricing model: Free tier

Tutorials & Demos

Frequently asked

Snowflake

BigQuery

PostgreSQL

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