DatriseAI-first ETL

Instagram to Looker Integration

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

How Datrise loads Instagram into Looker

Datrise syncs Instagram's media posts, stories, comments, followers, reach metrics, and profile insights into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Instagram: Organic profile content and audience insights.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Instagram entities map to Looker

Instagram entityLooker objectNotes
media postsinstagram_media_postsid PK · custom fields → flattened columns (nested fields expanded for modeling)
storiesinstagram_storiesid PK · linked to instagram_media_posts
commentsinstagram_commentsid PK · linked to instagram_media_posts
followersinstagram_followersid PK · linked to instagram_media_posts

FAQ

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

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

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

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

Early access

Connect Instagram to Looker the easy way

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