DatriseAI-first ETL

Attio Looker

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

How Datrise loads Attio into Looker

Datrise syncs Attio's objects, lists, records, notes, and relationship workflows 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

Attio: Modern CRM source for relationship and pipeline data.

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

How Attio entities map to Looker

Attio entityLooker objectNotes
objectsattio_objectsid PK · custom fields → flattened columns (nested fields expanded for modeling)
listsattio_listsid PK · linked to attio_objects
recordsattio_recordsid PK · linked to attio_objects
notesattio_notesid PK · linked to attio_objects

FAQ

How does Datrise handle Attio'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 Attio 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

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