DatriseAI-first ETL

Salesforce Commerce Cloud Google BigQuery

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

How Datrise loads Salesforce Commerce Cloud into Google BigQuery

Datrise syncs Salesforce Commerce Cloud'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

Salesforce Commerce Cloud: Enterprise CRM for complex sales, service, and revenue operations.

Google BigQuery: Serverless analytics warehouse on GCP.

How Salesforce Commerce Cloud entities map to Google BigQuery

Salesforce Commerce Cloud entityGoogle BigQuery objectNotes
contactssalesforce_commerce_cloud_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountssalesforce_commerce_cloud_accountsid PK · linked to salesforce_commerce_cloud_contacts
dealssalesforce_commerce_cloud_dealsid PK · linked to salesforce_commerce_cloud_contacts
activitiessalesforce_commerce_cloud_activitiesTIMESTAMP events

FAQ

How does Datrise handle Salesforce Commerce Cloud'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 Commerce Cloud 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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