DatriseAI-first ETL

Contentful to Looker Studio Integration

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

How Datrise loads Contentful into Looker Studio

Datrise syncs Contentful's entries, content types, assets, spaces, locales, and publish events 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

Contentful: Headless CMS for structured content delivery.

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

How Contentful entities map to Looker Studio

Contentful entityLooker Studio objectNotes
entriescontentful_entriesid PK · custom fields → flattened columns for chart fields
content typescontentful_content_typesid PK · linked to contentful_entries
assetscontentful_assetsid PK · linked to contentful_entries
spacescontentful_spacesid PK · linked to contentful_entries

FAQ

How does Datrise handle Contentful'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 Contentful 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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