Google Analytics Raw Data connector

The managed Google Analytics Raw Data connector in Lakeflow Connect allows you to ingest event-level data from Google Analytics 4 (GA4) into Databricks using BigQuery export.

What to know before you start

Topic	Why it matters
Databricks user persona	The workflow depends on your Databricks user persona: Single-user: An admin user creates a Unity Catalog connection and an ingestion pipeline. Multi-user: An admin user creates a connection for non-admin users to create pipelines with.
Authentication method	The steps to create a connection depend on the authentication method you choose.
Interface	The steps to create a pipeline depend on the interface.
Ingestion frequency	The pipeline schedule depends on your latency and cost requirements.
Common patterns	Depending on your ingestion needs, the pipeline might use configurations like history tracking, column selection, and row filtering. Supported configurations vary by connector. See Feature availability.

Start ingesting from Google Analytics

The following table provides an overview of the end-to-end Google Analytics Raw Data ingestion flow, based on user type:

User	Steps
Admin	Export your GA4 data to BigQuery. See Set up Google Analytics 4 and Google BigQuery for Databricks ingestion. Either: Use Catalog Explorer to create a connection to Google Analytics Raw Data so that non-admins can create pipelines. See Google Analytics Raw Data. Use the data ingestion UI to create a connection and a pipeline at the same time. See Ingest data from Google Analytics 4.
Non-admin	Use any supported interface to create a pipeline from an existing connection. See Ingest data from Google Analytics 4.

Feature availability

Feature	Availability
UI-based pipeline authoring	Supported
API-based pipeline authoring	Supported
Declarative Automation Bundles	Supported
Incremental ingestion	Supported
Unity Catalog governance	Supported
Orchestration using Databricks Workflows	Supported
SCD type 2	Supported
API-based column selection and deselection	Supported
API-based row filtering	Supported
Automated schema evolution: New and deleted columns	Supported
Automated schema evolution: Data type changes	Not supported
Automated schema evolution: Column renames	Supported Treated as a new column (new name) and deleted column (old name).
Automated schema evolution: New tables	Supported If you ingest the entire schema. See the limitations on the number of tables per pipeline.
Maximum number of tables per pipeline	250

Authentication methods

Authentication method	Availability
OAuth U2M	Supported
OAuth M2M	Not supported
OAuth (manual refresh token)	Not supported
Basic authentication (username/password)	Not supported
Basic authentication (API key)	Supported (API-only)
Basic authentication (service account JSON key)	Not supported