San Francisco, California – June 26, 2025 – RisingWave, the event stream data processing and management platform, today announced a new integration with Databricks, the Data and AI company. Building on its role as a key launch partner for Databricks’ new open-format initiatives, RisingWave now connects its powerful stream processing capabilities directly with the Databricks Data Intelligence Platform. Now, organizations can build end-to-end analytics and AI applications using the freshest possible data, more simply than ever before.
The integration leverages the open Apache Iceberg table format and Databricks' enhanced Unity Catalog. This creates a single, governed environment where real-time streaming data from RisingWave and batch data in Databricks can coexist and be queried together seamlessly.
Many organizations have a powerful lakehouse for batch processing and AI, but struggle with the complexity and cost of feeding it fresh data from real-time sources. This often creates a frustrating delay between when an event happens and when its data becomes useful. This integration addresses that challenge head-on, creating a powerful two-way data highway between RisingWave and Databricks.
Key Capabilities of the Integration:
This partnership unlocks two primary, powerful workflows:
Streaming ETL into Databricks-Managed Iceberg Tables
RisingWave can now be the high-performance streaming engine for your Databricks platform. Ingest data from sources like Kafka or Kinesis, use RisingWave to perform complex stateful processing with simple SQL, and sink the polished results directly into Databricks' Managed Iceberg Tables. It's the perfect solution for streaming ETL.Unified Governance of RisingWave-Managed Iceberg Tables via Unity Catalog
This integration extends Databricks Unity Catalog's governance to Iceberg tables managed by RisingWave. By registering these RisingWave tables (which can utilize JDBC, REST, or Glue catalogs for their own metadata management) as foreign tables in Databricks, they become discoverable and governable by Unity Catalog. This means materialized views generated in real-time by RisingWave are seamlessly accessible as standard tables within Databricks, adhering to the same security and governance frameworks as your native Databricks assets.
Why RisingWave for the Databricks Ecosystem?
RisingWave is not just another streaming processor; it's a PostgreSQL-compatible streaming database built for simplicity and power. For Databricks users, RisingWave introduces several key advantages:
Truly Simple Stream Processing: RisingWave’s core strength is creating materialized views on streaming data. Instead of writing complex Java/Scala code, your engineers can use familiar
CREATE MATERIALIZED VIEWstatements. RisingWave handles the hard work of keeping these views updated incrementally with extremely low latency.Cost-Effective Performance: Built from the ground up in Rust, RisingWave’s decoupled architecture for compute and storage delivers high performance while remaining highly resource-efficient. This makes real-time analytics affordable at any scale.
A Two-Way Street: The integration also lets teams enrich their real-time streams in RisingWave with historical or dimensional data stored in Databricks, enabling more context-aware decisions.
"We're thrilled to partner with Databricks and bring our streaming database to the heart of the modern data stack," said Rayees Pasha, Chief Product Officer at RisingWave. "The industry's shift to open standards like Iceberg is something we fully support, and Databricks is leading the charge. With this integration, we're giving Databricks users a way to finally conquer the complexity of stream processing and build real-time applications—from live dashboards to instant feature engineering—all with the simplicity of SQL."
"The Databricks Data Intelligence Platform is designed to help customers use all of their data, no matter where it comes from," said Jason Reid, Director of Product Management at Databricks. "Our open ecosystem relies on partners like RisingWave. Their best-in-class streaming database offers our customers a powerful, simple way to process real-time data and land it in the lakehouse, ready for AI and advanced analytics. We're excited to see what our joint customers will build."
New Opportunities for Data Teams
Combining RisingWave and Databricks empowers data teams to:
Develop Real-Time Features: Continuously compute features in RisingWave from live data and stream them to Databricks for immediate ML model use.
Modernize ETL/ELT with Streaming: Replace slow batch jobs with RisingWave's efficient, SQL-based streaming pipelines, delivering fresh, clean data to your Databricks lakehouse in seconds.
Power Live Lakehouse Dashboards: Drive operational dashboards and monitoring in Databricks with the freshest data, processed in real-time by RisingWave.
Unify Analytics: Analyze real-time operational insights from RisingWave alongside historical data in Databricks for a comprehensive view.
About RisingWave
RisingWave is an event stream processing and management platform. It offers an unified experience for real-time data ingestion, stream processing, data persistence, and low-latency serving. RisingWave is backed by a global community and is adopted by enterprises to build real-time analytics dashboards, streaming ETL pipelines, and live operational monitoring applications.
