Choosing the right table architecture is critical for maximizing performance, reducing costs, and ensuring data access across your analytics tools. This page explains the different table types and serving endpoints available in Lakehouse for Apache Iceberg, helping you choose the best option based on your write engines, read requirements, and management control needs.
Table formats by catalog or engine
Select a catalog or engine to learn about its supported table formats, metastore configuration, storage optimization capabilities, and engine interoperability.
Lakehouse runtime catalog
The Lakehouse runtime catalog manages Apache Iceberg tables through the Iceberg REST catalog endpoint and provides seamless read-write interoperability across Iceberg compatible engines (Spark, Flink, Trino) and BigQuery, while being backed by the industry-standard Iceberg REST catalog interface.
Supported table formats
Apache Iceberg V2 tables (GA) and V3 tables (Preview) are supported. Iceberg V1 tables aren't supported. Before you use existing V1 tables with Lakehouse for Apache Iceberg, you must upgrade them to a supported version. For more information, see Upgrade Iceberg V1 tables to V2.
Key features include:
- Metastore: Lakehouse runtime catalog.
- Storage: Cloud Storage.
- Storage optimization: Managed by you, or optionally by Google (Preview).
- Read and write access:
- Open source engines: read and write (GA)
- BigQuery: read/write (Preview)
- Use cases: Open lakehouse with high-performance, enterprise-grade storage for advanced analytics, streaming, and AI.
Hive metastore