DatriseAI-first ETL

Pipedrive Google BigQuery

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

How Datrise loads Pipedrive into Google BigQuery

Datrise syncs Pipedrive's deals, persons, organizations, activities, and stage movement analytics 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

Pipedrive: Pipeline-first CRM for sales teams.

Google BigQuery: Serverless analytics warehouse on GCP.

How Pipedrive entities map to Google BigQuery

Pipedrive entityGoogle BigQuery objectNotes
dealspipedrive_dealsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
personspipedrive_personsid PK · linked to pipedrive_deals
organizationspipedrive_organizationsid PK · linked to pipedrive_deals
activitiespipedrive_activitiesTIMESTAMP events

FAQ

How does Datrise handle Pipedrive'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 Pipedrive 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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