DatriseAI-first ETL

Propertybase to Databricks SQL Warehouse Integration

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

How Datrise loads Propertybase into Databricks SQL Warehouse

Datrise syncs Propertybase's leads, contacts, listings, opportunities, showings, and brokerage offices 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

Propertybase: Salesforce-based real estate CRM for brokerages.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Propertybase entities map to Databricks SQL Warehouse

Propertybase entityDatabricks SQL Warehouse objectNotes
leadspropertybase_leadsid PK · custom fields → VARIANT or STRUCT columns
contactspropertybase_contactsid PK · linked to propertybase_leads
listingspropertybase_listingsid PK · linked to propertybase_leads
opportunitiespropertybase_opportunitiesid PK · linked to propertybase_leads

FAQ

How does Datrise handle Propertybase'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 Propertybase 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

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