An enterprise-grade event streaming platform that ingests, processes, and manages real-time event data with PostgreSQL compatibility and Apache Iceberg™ integration.
RisingWave is an event streaming platform that continuously ingests, transforms, and serves event data in real time at scale. It provides a unified system for low-latency stream processing and Iceberg-native data management, enabling users to handle millions of events per second with built-in support for Apache Iceberg tables.
Data engineers, platform engineers, and developers building real-time analytics, monitoring systems, live dashboards, or feature engineering pipelines that require scalable and cost-effective event streaming.
Developers choose RisingWave for its PostgreSQL-compatible SQL interface, seamless integration with Apache Iceberg, and cost-efficient S3-based storage, which simplifies state management and provides high performance for complex real-time queries without manual tuning.
Event streaming platform for agentic AI. Continuously ingest, transform, and serve event streams in real time, at scale.
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Connects via PostgreSQL wire protocol, allowing seamless use with tools like psql and JDBC, and supports expressive SQL without manual state tuning, reducing integration effort.
Uses S3 as primary storage for tables and states, enabling high performance, fast recovery within seconds, and dynamic scaling to handle workload spikes, while elastic disk cache minimizes S3 access costs.
Directly hosts and manages Apache Iceberg REST catalogs, supporting continuous ingestion, built-in table maintenance like compaction, and ensuring interoperability with other query engines.
Performs incremental computations with end-to-end freshness under 100 ms and serves materialized views with 10–20 ms p99 query latency, ideal for real-time dashboards and monitoring.
Relies heavily on S3 for storage, which can introduce latency variability and additional costs if not optimized, and may not perform well in environments with poor object storage access.
As a unified platform integrating ingestion, processing, and serving, it requires managing multiple components, leading to a steeper learning curve and higher operational overhead compared to specialized tools.
Being a newer project, it has a smaller ecosystem with fewer connectors and community resources than established alternatives like Apache Flink, which could limit integration options and support.