DatriseAI-first ETL

Twenty CRM to Google BigQuery Integration

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

How Datrise loads Twenty CRM into Google BigQuery

Datrise syncs Twenty CRM's people, companies, opportunities, notes, tasks, and custom objects 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

Twenty CRM: Open-source CRM with a customizable object model.

Google BigQuery: Serverless analytics warehouse on GCP.

How Twenty CRM entities map to Google BigQuery

Twenty CRM entityGoogle BigQuery objectNotes
peopletwenty_peopleid PK · custom fields → JSON or nested/repeated (STRUCT) columns
companiestwenty_companiesid PK · linked to twenty_people
opportunitiestwenty_opportunitiesid PK · linked to twenty_people
notestwenty_notesid PK · linked to twenty_people

FAQ

How does Datrise handle Twenty CRM'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 Twenty CRM 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

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