DatriseAI-first ETL

Salesforce Databricks SQL Warehouse

AI-first ETL from Salesforce into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Salesforce into Databricks SQL Warehouse

Datrise syncs Salesforce's accounts, opportunities, contacts, tasks, and pipeline stage history into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Salesforce: Enterprise CRM and Customer 360 platform.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Salesforce entities map to Databricks SQL Warehouse

Salesforce entityDatabricks SQL Warehouse objectNotes
accountssalesforce_accountsid PK · custom fields → VARIANT or 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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Salesforce to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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