DatriseAI-first ETL

Salesforce Google BigQuery

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

How Datrise loads Salesforce into Google BigQuery

Datrise syncs Salesforce's accounts, opportunities, contacts, tasks, and pipeline stage history 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

Salesforce: Enterprise CRM and Customer 360 platform.

Google BigQuery: Serverless analytics warehouse on GCP.

How Salesforce entities map to Google BigQuery

Salesforce entityGoogle BigQuery objectNotes
accountssalesforce_accountsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
opportunitiessalesforce_opportunitiesid PK · linked to salesforce_accounts
contactssalesforce_contactsid PK · linked to salesforce_accounts
taskssalesforce_tasksid PK · linked to salesforce_accounts

FAQ

How does Datrise handle Salesforce'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 Salesforce 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.

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