🌊 Event Streaming for Agentic AI
RisingWave is an event streaming platform for agentic AI. It continuously ingests data from databases, event streams, and webhooks, processes it incrementally, and serves fresh results at low latency, replacing the traditional event streaming stack (e.g., Debezium + Kafka + Flink + serving DB) with a single system.
Try it out in 60 seconds
curl -L https://risingwave.com/sh | sh
For Docker, Kubernetes, and other options, see the quick start guide.
The problem
Agents and real-time applications need data that is always fresh and queryable at low latency. The standard approach chains together Debezium for CDC, Kafka for transport, Flink for processing, and a database for serving. Each hop adds latency and each system adds operational overhead.
RisingWave replaces the whole stack: ingest, process, serve, store.
How it works
Ingest from any source
RisingWave ingests across the full data spectrum:
- Webhooks: HTTP-based event ingestion from SaaS applications and external systems
- Database changes: native CDC from PostgreSQL, MySQL, and others via transaction log reading
- Event streams: Kafka, Pulsar, Kinesis, and other message brokers
- Historical data: batch ingestion from S3, data warehouses, and other storage systems
All sources are unified under the same SQL interface. Streams and tables can be joined freely.
Process continuously
RisingWave performs incremental computation over ingested data. When upstream data changes, only the affected results are recomputed. End-to-end freshness is under 100 ms.
This is the core mechanism behind everything RisingWave does: materialized views that are always up to date, without full recomputation on every query.
Serve at low latency
Query results are maintained in RisingWave's internal row store and served at 10-20 ms p99 latency. Agents and applications query this layer directly using standard SQL. No polling, no cache warming, no TTL management.
Store in Apache Iceberg™
For long-term retention and analytical access, RisingWave writes to Apache Iceberg™ tables. It hosts the Iceberg REST catalog directly and handles table maintenance — compaction, small-file optimization, snapshot cleanup — without external tooling. Iceberg queries are executed via Apache DataFusion, a vectorized query engine. Because Iceberg is an open format, data is also readable by Spark, Trino, DuckDB, and other engines.
The row store and Iceberg layer serve different purposes: the row store is for low-latency serving, Iceberg is for durable, open-format storage and analytical queries. RisingWave manages both.
Use cases
- Monitoring and alerting: continuous evaluation of streaming metrics against thresholds
- Feature stores: batch and streaming features computed over the same pipeline, served from the same system
- Live dashboards: materialized views updated incrementally, no scheduled refreshes
- Real-time enrichment: live events joined with historical reference data in-flight, before delivery downstream
- Streaming lakehouses: continuous, exactly-once ingestion into open-format tables with automated compaction and snapshot management
Design decisions
Ultimate cost efficiency
Internal state, tables, and materialized views are stored in object storage (S3 or equivalent), which is roughly 100x cheaper than RAM. This enables elastic scaling without data rebalancing and failure recovery in seconds. For latency-sensitive workloads, elastic disk cache pins hot data on local SSD or EBS, keeping p99 query latency at 10-20 ms.
Native experience for both humans and agents
RisingWave connects via the PostgreSQL wire protocol and works with psql, JDBC, and any Postgres-compatible tooling. For agents, RisingWave provides a MCP server, a CLI, and Skills, so agents can query and operate RisingWave without custom integration.
Openness
RisingWave natively integrates with Apache Iceberg™ for continuous stream ingestion, direct reads via DataFusion, and automated table maintenance. Data in Iceberg is in an open format and accessible to any compatible query engine.
Deployment
RisingWave Cloud is the managed option.
For self-hosted:
Community
Join us on Slack for questions, discussions, and contributions.
Telemetry
RisingWave uses Scarf for anonymized installation analytics and collects anonymous usage statistics to improve the product. Both can be opted out. See the telemetry documentation for details.
License
Apache License 2.0. See LICENSE.
Contributing
See the RisingWave Developer Guide.
