Google Cloud Lakehouse for Apache Iceberg
The facts below are what the maker publishes about Google Cloud Lakehouse for Apache Iceberg, checked and dated. Our editors have not written about it yet.
Plans and prices
From Google Cloud Lakehouse for Apache Iceberg's own pricing page, checked 20 Sep 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Metadata storage free tier | Priced on request | 1 GiB metadata storage · Free tier includes 1 GiB of metadata storage per month |
| Metadata storage | Priced on request | 1 GiB free metadata storage included · Starting at $0.04 per GiB per month |
| Table management | Priced on request | Automatic table storage optimization · Adaptive file sizing · Automatic clustering · Garbage collection · BigQuery metadata generation · Starting at $0.12 per DCU-hour |
| Catalog federation metadata refresh | Priced on request | 0 to 5,001 operations free per month per account · Above 5,001 operations per month per account |
| Metadata storage included tier | Free | Lakehouse runtime catalog metadata · 1 GiB per month |
| Metadata storage overage | $0.04 a month | metadata beyond included 1 GiB · per GiB per month |
| Lakehouse table management | $0.12 a month | automatic file sizing · clustering · garbage collection · BigQuery metadata generation · per DCU-Hour |
| Class B metadata access | $0.90 a month | read · delete · getIamPolicy · per million operations above 50,001 |
| Class A metadata access | $6 a month | create · register · list · update · setIamPolicy · per million operations above 5,001 per month/account |
The facts
Each one read from Google Cloud Lakehouse for Apache Iceberg's own pages, with the date it was checked.
What it does
- AI features
- BigQuery ObjectRefs combine structured Iceberg data with unstructured objects for multimodal, vector and graph analytics.checked 15 Oct 2026
- API
- The Lakehouse API is a serverless, highly available metastore API exposing REST resources for catalogs and data-sharing objects.checked 15 Oct 2026
- Automated maintenance
- Lakehouse can automate file sizing, compaction, clustering, garbage collection, partitioning and history-based optimization.checked 15 Oct 2026
- BigQuery analytics
- BigQuery supports vectorized execution, partition pruning, multi-statement transactions and SQL DDL/DML over Lakehouse Iceberg tables.checked 15 Oct 2026
- Compliance
- Google Cloud lists ISO 27001, ISO 27017, ISO 27018, ISO 27701, SOC 1/2/3, PCI DSS, FedRAMP, GDPR and HIPAA offerings or alignments.checked 15 Oct 2026
- Cross-cloud federation
- Preview federation can expose data cataloged in AWS Glue, Databricks Unity Catalog and Snowflake Horizon Catalog to BigQuery and Spark without moving the data.checked 15 Oct 2026
- Deployment model
- Lakehouse runtime catalog is fully managed and removes the need to operate a self-hosted Hive Metastore.checked 15 Oct 2026
- Developer documentation
- Google provides introductory tutorials, quickstarts, reference material, code samples, engine-specific guides and troubleshooting documentation.checked 15 Oct 2026
- Governance
- Knowledge Catalog integration supports centralized governance policies, semantic search, lineage and quality checks across engines.checked 15 Oct 2026
- Infrastructure as code
- The Google Terraform provider provisions Lakehouse runtime catalog resources, including Iceberg REST catalog endpoints.checked 15 Oct 2026
- Interoperability
- The Iceberg REST catalog provides read and write interoperability among BigQuery, Managed Service for Apache Spark, Spark, Flink, Trino and other compatible engines.checked 15 Oct 2026
- Legal entity
- Google Cloud's contracting-entity page identifies Google LLC for the United States and other uncovered locations.checked 15 Oct 2026
- License
- The Apache Iceberg project is distributed under the Apache License 2.0; Google Cloud Lakehouse itself is a proprietary managed service.checked 15 Oct 2026
- Limit: API access
- The BigQuery tabledata.list method cannot retrieve rows directly from runtime-catalog tables.checked 15 Oct 2026
- Limit: orchestration and scheduling
- TrustRadius reviewers report that orchestration and scheduled-query workflows can be awkward and require practice.checked 15 Oct 2026
- Limit: query performance
- Google warns that BigQuery queries over runtime-catalog tables may be slower than queries over standard BigQuery tables.checked 15 Oct 2026
- Limit: support
- Gartner's displayed dislikes include frustration when working with Google support.checked 15 Oct 2026
- Limit: table operations
- The runtime catalog does not support table renaming, clustering, extract jobs, database views or Iceberg views.checked 15 Oct 2026
- Open REST interface
- The Iceberg endpoint implements the open-source Apache Iceberg REST Catalog API and supports credential vending for fine-grained access.checked 15 Oct 2026
The company
- Company founding and headquarters
- Google says it was officially born in 1998 and moved its headquarters to Mountain View, California; LinkedIn lists Mountain View as headquarters.checked 15 Oct 2026
- Company size
- LinkedIn lists Google as a public software-development company with more than 10,001 employees.checked 15 Oct 2026
- Founded
- 1998checked 23 Sep 2026
- Headquarters
- Mountain View, California, United Stateschecked 23 Sep 2026
Money
- Limit: cost visibility
- BigQuery dry runs can report zero processed bytes for runtime-catalog queries even though the executed query incurs processing cost.checked 15 Oct 2026
Alternatives to Google Cloud Lakehouse for Apache Iceberg
- 1
Databricks Notebooks
Free planAndroidWebAPILinux8.2$0.07 usage-based - 2
Starburst Galaxy
Free planWebAPILinux7.1Free plan - 3
Starburst Data Platform
Free planWebAPILinuxSelf-hosted6.8Free plan - 4
Bauplan
Free planWebAPI6.7From $700 a month - 5
Datastrato Iceberg Lakehouse Platform
Open sourceAPILinuxSelf-hosted6.6 - 6
LavaLake
Free planWebAPISelf-hosted6.6From €249 a month