Enterprise-grade event streaming platform that continuously ingests, processes, and serves real-time data with Apache Iceberg™ integration.
RisingWave is an enterprise-grade event streaming platform that ingests, processes, and manages real-time event data. It combines ultra-low-latency stream processing with native Apache Iceberg data management in a unified system, enabling real-time analytics, monitoring, and data enrichment at scale.
Data engineers and platform teams building real-time analytics, monitoring, and data enrichment pipelines, particularly those operating in cloud-native environments with a focus on cost efficiency and interoperability with open table formats.
Developers choose RisingWave for its PostgreSQL compatibility, which simplifies integration and eliminates manual state tuning, and its cost-effective design leveraging S3 as primary storage and native Apache Iceberg support for an open, interoperable lakehouse architecture.
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 with a familiar SQL dialect, allowing seamless use of tools like psql and JDBC, and eliminates manual state tuning as highlighted in the README.
Uses S3 as primary storage for tables and processing state, enabling high performance, fast recovery, and dynamic scaling without expensive in-memory clusters, as described in the key design decisions.
Directly hosts and manages Apache Iceberg REST catalog, supporting continuous ingestion and built-in table maintenance like compaction, which promotes interoperability with open data lakehouses.
Performs incremental computations with end-to-end freshness under 100 ms and serves materialized views with 10–20 ms p99 latency, ideal for real-time dashboards and monitoring.
Integrates real-time ingestion, stream processing, and low-latency serving in a single system, simplifying architecture and reducing operational overhead for unified analytics.
Heavy reliance on S3 or equivalent object storage can introduce vendor lock-in and increase egress costs in multi-cloud or hybrid environments, limiting flexibility.
While it offers SQL and Python interfaces, support for complex custom functions and plugins is less mature compared to established frameworks like Flink, restricting advanced processing logic.
Production setups, especially on Kubernetes, require careful configuration and management, as indicated by the detailed deployment guides, which can steepen the learning curve.
As a relatively new platform, it has a smaller community and fewer third-party integrations compared to alternatives, which might affect troubleshooting and extensibility.