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Internal Iceberg tables are created and managed directly by RisingWave. They behave like standard RisingWave tables but store their data in the Apache Iceberg format on object storage. This allows you to persist computed or aggregated results in an open, queryable format that can be accessed by any Iceberg-compatible engine. RisingWave acts as the ingestion and transformation layer in modern data lakehouse architectures:
  • Ingestion can be implemented as ETL or ELT pipelines that continuously write data into Iceberg tables.
  • Transformation is powered by RisingWave’s incremental materialized views, enabling efficient, cascading updates across multiple layers of derived data.
This design makes RisingWave suitable for implementing the Medallion architecture. Users can flexibly use RisingWave to build real-time pipelines, maintain derived datasets, or serve analytical workloads on top of Iceberg. RisingWave in the Medallion architecture For example, you can implement the three layers of the Medallion architecture as follows:
  • Use internal Iceberg tables as the Bronze layer for raw data storage.
  • Use materialized views as the Silver layer to filter and transform data.
  • Use cascading materialized views (built on top of the Silver layer MVs) as the Gold layer to aggregate and enrich data for analytics.

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Hosted catalog services

RisingWave provides two hosted catalog options for managing Iceberg metadata, schema versions, and table state:
  • JDBC hosted catalog — backed by RisingWave’s internal PostgreSQL-compatible metastore. See JDBC hosted catalog.
  • REST hosted catalog — powered by Lakekeeper and compatible with the Iceberg REST catalog API. See REST hosted catalog.
Both options allow external Iceberg engines to read and write RisingWave-managed tables using standard Iceberg protocols. If you prefer to use an existing metadata system, RisingWave also supports external catalogs such as AWS Glue, Hive Metastore, or Nessie. See Catalog configuration for details.

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Compaction service

RisingWave provides a managed compaction service that helps maintain table health by performing compaction and snapshot expiration.
  • Compaction: Merges small data files into larger, optimized files to improve read performance.
  • Snapshot Expiration: Removes old, unneeded snapshots and their associated data files to reclaim storage space.
You can enable automatic maintenance to run periodically or trigger it manually using the VACUUM command. Using RisingWave’s service is optional, and you can also connect an external compactor from providers like Databricks, Tabular, or AWS EMR, or use a self-hosted Spark job. For complete details on configuration, see the Iceberg table maintenance.

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Catalog and compaction summary

ComponentRisingWave Native OptionsAlternative OptionsDescription
Catalog serviceRisingWave hosted JDBC and REST catalogsGlue, Hive, Nessie, or custom REST catalogsStores metadata and schema information
Compaction serviceRisingWave’s built-in compaction serviceExternal services (Databricks, Tabular, EMR) or self-hosted SparkMerges small files and expires old snapshots
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