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RisingWave is an open-source streaming database that is wire-compatible with PostgreSQL. It provides a unified platform for real-time data ingestion from sources like Kafka, CDC, and S3, stream processing via incrementally maintained materialized views, and low-latency query serving. RisingWave also provides native Apache Iceberg lakehouse management. All persistent data is stored in object storage (S3, GCS, Azure Blob) for cost efficiency and elastic scalability. You can deploy RisingWave via Docker, Kubernetes, or the fully managed RisingWave Cloud.
RisingWave architecture diagram showing unified streaming data platform with online stream processing and offline Iceberg lakehouse management

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Unified platform for streaming data

RisingWave delivers a unified streaming data platform for both online and offline workloads. For online workloads, it provides ultra-low-latency stream processing. For offline workloads, it offers Iceberg-native data management.

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Online: Low-latency stream processing and analytics

RisingWave integrates real-time stream processing and low-latency serving in a single system. It continuously ingests data from streaming and batch sources, performs incremental computations across streams and tables with end-to-end freshness under 100 ms. Materialized views can be served directly within RisingWave with 10–20 ms p99 query latency, or delivered to downstream systems.

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Offline: Iceberg lakehouse ingestion, transformation, and management

RisingWave treats Apache Iceberg™ as a first-class citizen. It directly hosts and manages the Iceberg REST catalog, allowing users to create and operate Iceberg tables through a PostgreSQL-compatible interface. RisingWave supports two write modes: Merge-on-Read (MoR) and Copy-on-Write (CoW), to suit different ingestion and query patterns. It also provides built-in table maintenance capabilities, including compaction, small-file optimization, vacuum, and snapshot cleanup, ensuring efficient and consistent data management without external tools or pipelines. Plug: Nimtable is an observability tool developed by RisingWave for easily exploring and managing Iceberg tables.

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Key design decisions

RisingWave is heavily optimized for ease of use and cost efficiency.

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PostgreSQL compatibility

RisingWave is wire-compatible with PostgreSQL, enabling:
  • Expressive SQL: Supports structured, semi-structured, and unstructured data with a familiar SQL dialect.
  • Seamless integration: Works with psql, JDBC, and any PostgreSQL-compatible tool via the PostgreSQL wire protocol.

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Object storage as primary storage

RisingWave stores tables, materialized views, and internal states of stream processing jobs in object storage (e.g., S3, GCS, MinIO), providing:
  • High performance: Optimized for complex queries, including joins and time windowing, without the need for complex state management.
  • Fast recovery: Restores from system failures within seconds.
  • Dynamic scaling: Instantly adjusts resources to handle workload spikes.
Beyond caching hot data in memory, RisingWave supports elastic disk cache, a powerful performance optimization that uses local disks or EBS for efficient data caching. This minimizes access to object storage like S3, lowering processing latency and cutting object store access costs.

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Apache Iceberg™ native support

RisingWave natively integrates with Apache Iceberg™, enabling continuous ingestion of streaming data into Iceberg tables. It can also read directly from Iceberg, perform automatic compaction, and maintain table health over time. Since Iceberg is an open table format, results are accessible by other query engines — making storage not only cost-efficient, but interoperable by design.

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Classic use cases

RisingWave is particularly effective for the following use cases:

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Monitoring and alerting

For business-level monitoring where every transaction or event matters. RisingWave continuously evaluates event streams from trading, payments, and fraud detection systems to identify anomalies, policy violations, or performance issues, and triggers alerts within seconds.

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Real-time data enrichment

For event-driven analytics and operational pipelines that join and enrich multiple data streams such as trades, orders, quotes, or reference data in real time. RisingWave supports multi-way joins, schema evolution, deduplication, and automatic handling of late or out-of-order events.

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IoT and telemetry pipelines

For high-frequency telemetry and device data in connected environments such as logistics, energy, and manufacturing. RisingWave aggregates sensor readings, computes rolling metrics, and streams processed telemetry to monitoring dashboards or AI models for anomaly detection and optimization.

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Streaming lakehouse

For real-time lakehouse architectures built on open formats such as Apache Iceberg. RisingWave continuously ingests data from Postgres, Kafka, and other operational systems, performs transformations, compacts small files, manages schema evolution, and writes data into Iceberg tables for unified analytics and long-term retention.

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See also