Looker to Tableau Integration

How Datrise loads Looker into Tableau

Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Looker: BI platform with a modeled semantic layer.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Looker entities map to Tableau

Looker entity	Tableau object	Notes
looks	`looker_looks`	id PK · custom fields → flattened columns for Tableau fields
dashboards	`looker_dashboards`	id PK · linked to looker_looks
explores	`looker_explores`	id PK · linked to looker_looks
queries	`looker_queries`	id PK · linked to looker_looks

FAQ

How does Datrise handle Looker's custom fields in Tableau?

Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.

How does the Looker to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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