LookML to Google BigQuery Integration

How Datrise loads LookML into Google BigQuery

Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

LookML: Semantic model definitions behind Looker content.

Google BigQuery: Serverless analytics warehouse on GCP.

How LookML entities map to Google BigQuery

LookML entity	Google BigQuery object	Notes
models	`lookml_models`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
explores	`lookml_explores`	id PK · linked to lookml_models
views	`lookml_views`	id PK · linked to lookml_models
dimensions	`lookml_dimensions`	id PK · linked to lookml_models

FAQ

How does Datrise handle LookML's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the LookML to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

Connect LookML to Google BigQuery the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.