DatriseAI-first ETL

Confluence to Looker Studio Integration

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

How Datrise loads Confluence into Looker Studio

Datrise syncs Confluence's spaces, pages, blog posts, comments, attachments, and page versions 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

Confluence: Team wiki and documentation workspace.

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

How Confluence entities map to Looker Studio

Confluence entityLooker Studio objectNotes
spacesconfluence_spacesid PK · custom fields → flattened columns for chart fields
pagesconfluence_pagesid PK · linked to confluence_spaces
blog postsconfluence_blog_postsid PK · linked to confluence_spaces
commentsconfluence_commentsid PK · linked to confluence_spaces

FAQ

How does Datrise handle Confluence'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 Confluence 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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