Google BigLake API
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For Agents
Manage Apache Iceberg catalogs, databases, and tables in Google Cloud BigLake Metastore for open-source data lakes.
Use for: I need to create an Iceberg catalog in BigLake Metastore, List every database in a catalog and their table counts, Rename an Iceberg table without dropping and recreating it, Patch a table's labels for cost-tracking purposes
Not supported: Does not run queries, store table data, or transform records — use for managing BigLake Metastore catalogs, databases, and Iceberg table metadata only.
The BigLake API provides programmatic access to BigLake Metastore, a serverless metastore for open-source data formats — primarily Apache Iceberg tables — that can be queried from BigQuery and Spark. It exposes operations to manage catalogs, databases, and tables, including a rename operation for schema evolution. Resources are scoped to a project and location, and the metastore makes the same data discoverable to multiple compute engines without per-engine setup. The API does not run queries or process data; it manages metadata only.
Install Jentic One Beta
Connect the BigLake API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the BigLake API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with BigLake API.
Create and list catalogs in BigLake Metastore at /v1/{+parent}/catalogs
Create and list databases inside a catalog via /v1/{+parent}/databases
Create, get, patch, and delete Iceberg table entries scoped to a database
Rename a metastore resource via /v1/{+name}:rename to support schema evolution
Page through large catalog and database listings using pageToken on the list endpoints
Patch table or database metadata to update labels and references
Use Cases
Patterns agents use BigLake API for, with concrete tasks.
★ Iceberg-Based Data Lake on Google Cloud
Run an open Iceberg-format data lake on Cloud Storage and make the tables queryable from BigQuery and Spark by registering them in BigLake Metastore. The API is the single integration surface for catalog and database management, so onboarding a new domain is a matter of creating a catalog plus the relevant databases and tables.
POST a catalog create request to /v1/{parent}/catalogs and then create the underlying databases for each domain.
Schema Evolution Without Data Movement
Rename Iceberg tables and databases as schemas evolve without copying data. The dedicated rename endpoint updates the metastore reference, leaving the underlying object-storage data in place and avoiding rewrite cost. Useful during refactors that consolidate or split domains.
POST a new name to /v1/{name}:rename for the source resource and verify the response.
Cross-Engine Data Discovery
Make the same Iceberg datasets discoverable to BigQuery, Dataproc Spark, and external Iceberg-aware engines from one metastore. The API exposes list endpoints suitable for building a data catalog UI or feeding lineage tools without parsing object-storage layouts.
List all catalogs and their databases, and feed the results into a downstream data catalog tool as a metadata sync.
AI Agent Data Lake Hygiene via Jentic
Use an AI agent to keep BigLake catalogs tidy by listing resources, identifying stale entries, and renaming or deleting them on a schedule. Through Jentic the agent finds the rename and delete operations by intent and runs them with project credentials supplied at execute time, removing the need for SDK setup in the agent.
Use Jentic search for 'rename a table in BigLake Metastore', load the schema for /v1/{name}:rename, and execute it with the new name.
Key Endpoints
10 endpoints — the biglake api provides programmatic access to biglake metastore, a serverless metastore for open-source data formats — primarily apache iceberg tables — that can be queried from bigquery and spark.
METHOD
PATH
DESCRIPTION
GET
/v1/{+name}
Get a catalog, database, or table by resource name
PATCH
/v1/{+name}
Update a database or table's mutable fields
DELETE
/v1/{+name}
Delete a catalog, database, or table
POST
/v1/{+name}:rename
Rename a metastore resource
GET
/v1/{+parent}/catalogs
List catalogs in a project and location
POST
/v1/{+parent}/catalogs
Create a catalog
GET
/v1/{+parent}/databases
List databases in a catalog
POST
/v1/{+parent}/databases
Create a database
GET
/v1/{+name}
Get a catalog, database, or table by resource name
PATCH
/v1/{+name}
Update a database or table's mutable fields
DELETE
/v1/{+name}
Delete a catalog, database, or table
POST
/v1/{+name}:rename
Rename a metastore resource
GET
/v1/{+parent}/catalogs
List catalogs in a project and location
POST
/v1/{+parent}/catalogs
Create a catalog
GET
/v1/{+parent}/databases
List databases in a catalog
POST
/v1/{+parent}/databases
Create a database
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Service account keys with BigLake scopes are encrypted in the Jentic vault. Agents only see scoped, short-lived access tokens and never the raw JSON credential.
Intent-based discovery
Agents search Jentic with intents like 'create a BigLake catalog' or 'rename an Iceberg table' and Jentic returns the matching operation with its input schema for direct execution.
Time to first call
Direct integration: 1-2 days for IAM setup, catalog design, and SDK wiring. Through Jentic: under 1 hour once the credential is in the vault.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using BigLake API through Jentic.
What authentication does the BigLake API use?
The API uses Google OAuth 2.0 with cloud-platform scope. Service accounts must hold the roles/biglake.* IAM role on the project. Through Jentic the service account credential is encrypted in the vault and only short-lived access tokens reach the agent.
Can I rename an Iceberg table through the BigLake API?
Yes. POST a new name to /v1/{name}:rename on the table, database, or catalog resource. Renaming updates the metastore reference without moving the underlying object-storage data.
How do I create a new catalog through Jentic?
Search Jentic for 'create a BigLake catalog'. Jentic returns POST /v1/{parent}/catalogs with the input schema; execute it with the catalog name and Jentic supplies the credential from the vault. The catalog is then ready for databases.
What are the rate limits for the BigLake API?
Numeric limits are not declared in the spec; quotas are enforced per project on biglake.googleapis.com. Review the project's quota dashboard before driving large numbers of metadata changes.
Does BigLake run queries against the data?
No. BigLake is a metastore. Queries run in BigQuery, Dataproc Spark, or another Iceberg-aware engine pointed at the same metastore. This API exposes only the metadata surface.
Can I list catalogs across multiple projects in one call?
No. The list endpoint is scoped to a parent in the form projects/{project}/locations/{location}. Iterate the projects you care about and call the list endpoint for each.
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