Interact with borderless Lakehouse data in BigQuery
Borderless Lakehouse is a storage engine that unites Google Cloud and open source services to create a unified interface for advanced analytics and AI. It provides the foundation to build an open, managed, and high-performance lakehouse with automated data management and built-in governance using Apache Iceberg.
When you create a table in Lakehouse, it is automatically queryable from BigQuery and is visible on the BigQuery page of the Google Cloud console. Your Lakehouse namespaces and schemas are also automatically mapped to BigQuery datasets.
Differences between Lakehouse resources and other BigQuery resources
The following are key differences between Lakehouse and standard BigQuery resources:
- Lakehouse datasets appear in the BigQuery page of the Google Cloud console next to the water icon.
- You can't modify Lakehouse resources from BigQuery.
- Lakehouse resources have additional metadata in their respective Details section.
Iceberg table capabilities comparison
Use the following table to compare capabilities between Apache Iceberg tables managed by the Lakehouse runtime catalog and Apache Iceberg tables managed by BigQuery.
| Capability | Apache Iceberg tables managed by Lakehouse runtime catalog | Apache Iceberg tables managed by BigQuery |
|---|---|---|
| Catalog | Lakehouse runtime catalog (Iceberg REST catalog compatible) | BigQuery |
| Storage | Cloud Storage | Cloud Storage |
| Accessible through the Iceberg REST catalog endpoint | Yes | Yes, using BigQuery catalog federation |
| Read/Write Interoperability | ||
| BigQuery read queries (SELECT, BQML, AI functions) | Supported | Supported |
| BigQuery DML (INSERT, UPDATE, DELETE, MERGE) | Supported (Preview) | Supported (GA) |
| OSS engine reads | Supported (GA) | Supported (GA) (using BigQuery catalog federation) |
| OSS engine writes | Supported (GA) | Not supported |
| OSS engine streaming writes (Kafka, Spark, Dataflow with Iceberg I/O sink) | Supported (GA) | Not supported |
| Managed and Advanced Capabilities | ||
| Table management (compaction, garbage collection) | Supported (Preview) | Supported (GA) |
| BigQuery streaming writes (storage write API) | Not supported | Supported (GA) |
| Pub/Sub streaming/subscription, Dataflow streaming with BigQuery I/O sink | Not supported | Supported (GA) |
| BigQuery Change Data Capture (CDC) | Not supported | Supported (Private Preview) |
| BigQuery multi-statement transactions | Not supported | Supported (Preview) |
| Managed disaster recovery | Not supported | Not supported |
| Search index | Not supported | Not supported |
| Vector index (including auto embedding generation) | Not supported | Not supported |
| Time Travel | ||
| Time travel (using OSS engines) | Flexible (configured through table properties) | Not supported |
| Time travel (using BigQuery) | Limited to 7 days | Limited to 7 days |
| Snapshot history and rollback to previous snapshot | Supported | Not supported |
| Governance, Security and Sharing | ||
| BigQuery Authorized Views | Not supported | Not supported |
| BigQuery column level security | Not supported | Supported (GA) |
| BigQuery data masking and policy tags | Not supported | Supported (GA) |
| BigQuery row level security | Not supported | Not supported |
| Analytics Hub integration | Not supported | Supported |
| Knowledge catalog capabilities | ||
| Metadata cataloging, search and discovery | Supported | Supported |
| Lineage | Supported | Supported |
| Data quality/profiling | Supported | Supported |
| Insights | Supported | Supported |
| AI-based column and table descriptions generation | Supported | Supported |
Access cross-cloud data
The cross-cloud data access capability of Lakehouse lets you query data stored with other cloud providers directly from BigQuery, without migrating files or building complex ETL pipelines. For configuration information, see Query remote data.
What's next
- Learn more about borderless Lakehouse.