DatriseAI-first ETL

PipeRun Google BigQuery

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

How Datrise loads PipeRun into Google BigQuery

Datrise syncs PipeRun's contacts, accounts, deals, activities, and lifecycle events 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

PipeRun: CRM widely used in Latin America for sales pipeline and customer ops.

Google BigQuery: Serverless analytics warehouse on GCP.

How PipeRun entities map to Google BigQuery

PipeRun entityGoogle BigQuery objectNotes
contactspiperun_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountspiperun_accountsid PK · linked to piperun_contacts
dealspiperun_dealsid PK · linked to piperun_contacts
activitiespiperun_activitiesTIMESTAMP events

FAQ

How does Datrise handle PipeRun'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 PipeRun 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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