DatriseAI-first ETL

Fauna to Looker Studio Integration

AI-first ETL from Fauna into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Fauna into Looker Studio

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Fauna: Distributed document-relational database.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Fauna entities map to Looker Studio

Fauna entityLooker Studio objectNotes
collectionsfauna_collectionsid PK · custom fields → flattened columns for chart fields
documentsfauna_documentsid PK · linked to fauna_collections
indexesfauna_indexesid PK · linked to fauna_collections
functionsfauna_functionsid PK · linked to fauna_collections

FAQ

How does Datrise handle Fauna's custom fields in Looker Studio?

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

How does the Fauna to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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