DatriseAI-first ETL

Attio Google BigQuery

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

How Datrise loads Attio into Google BigQuery

Datrise syncs Attio's objects, lists, records, notes, and relationship workflows into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Attio: Modern CRM source for relationship and pipeline data.

Google BigQuery: Serverless analytics warehouse on GCP.

How Attio entities map to Google BigQuery

Attio entityGoogle BigQuery objectNotes
objectsattio_objectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Attio to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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