Streaming Lakehouse - RisingWave: Streaming Database Built on Open Standards - RisingWave: Streaming Database Built on Open Standards

RisingWave Open Lake
Managed Iceberg™ Tables,
Built for Streaming

Build your own Apache Iceberg-based lakehouse for fast-moving data today. No hassle with catalogs, compaction, or observability. Full control, open interoperability, and low TCO.

Trusted by 1,000+ Data-Driven Organizations

to harness continuous insights from both live and historical data.

Trusted by 1,000+ Data-Driven Organizations to harness continuous insights from both live and historical data.

Discover Why Businesses Rely on RisingWave to Build Streaming Lakehouses

RisingWave lets you use Postgres-style SQL to ingest, transform, manage, and query Apache Iceberg tables—no custom pipelines, no vendor-specific DSLs. You work with Iceberg just like you would with a regular Postgres database.

Under the hood, RisingWave is built in Rust, engineered for safety and performance, and battle-tested in production across mission-critical streaming workloads. It’s a system you can trust to stay fast, consistent, and resilient—no matter how messy the data gets.

70%

Lower Infrastructure Costs

10x

Productivity

Better Performance

Your Data, Your Bucket, Your Ownership

Bring your own Iceberg tables—or use RisingWave’s Managed Iceberg Tables, tailored for streaming workloads. Either way, your data stays in your cloud VPC. No hidden storage. No hidden fees.

RisingWave is built on open standards. You’re free to ingest with us, query with others, or mix and match engines like Trino, Spark, and DuckDB. Your Iceberg tables remain fully portable and queryable—always.

We handle the hard parts: streaming ingestion, compaction, schema evolution, and exactly-once guarantees—so you can stay focused on building.

read the documentation

Managed Apache Iceberg™ Tables: Tailored for Your Streaming Data

RisingWave goes beyond real-time ETL/ELT for lakehouses—it also compacts data and delivers high-performance lake analytics. No vendor lock-in—your Iceberg lake is yours, and we help you integrate seamlessly with the open data ecosystem.

Ingesting and Transforming Streaming Data in Real Time

Continuously track and capture data changes from Kafka or database Change Data Capture (CDC) streams. Transform updates on the fly and seamlessly sync them to your data lakehouse—whether Iceberg or Delta Lake—ensuring fresh, query-ready data in real time, without the complexity of batch processing.

Compact Parquet Files Whenever Needed

Continuously compact Parquet files in Iceberg to optimize performance and minimize storage overhead. Maintain a fast, efficient, and query-ready data lakehouse—eliminating the complexity of manual maintenance.

Run Lake Analytics Using the Same Engine

Run high-performance analytics directly on your Iceberg lake without the need for external query engines. With built-in support for SQL-based querying, RisingWave enables seamless real-time insights, ad-hoc exploration, and complex analytics—all while ensuring optimal storage and compute efficiency. Whether you're processing streaming updates or historical data, RisingWave makes Iceberg analytics fast, efficient, and scalable.

Rich Set of Connectors

RisingWave offers purpose-built streaming connectors equipped with built-in intelligence to detect back pressure, enabling efficient data ingestion from numerous sources in a decentralized manner.

Composable Data Pipelines

Live data has short shelf life. Incremental updates are triggered automatically in RisingWave to guarantee always fresh insights letting users get the most value of their data sets.

Incremental Processing for Ultra Low Latency

RisingWave makes data pipelines composable, allowing tables and views generated by one query to be seamlessly used as inputs for downstream queries.

Native Data Lake Formats Support

Interoperability is a core design principle of RisingWave. As Iceberg and Delta increasingly become the de facto standards for data lakehouse table formats, RisingWave provides robust read and write support for both.