DatriseAI-first ETL

Keap Databricks SQL Warehouse

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

How Datrise loads Keap into Databricks SQL Warehouse

Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows 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

Keap: SMB CRM with pipeline automation, email, and appointment flows.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Keap entities map to Databricks SQL Warehouse

Keap entityDatabricks SQL Warehouse objectNotes
SMB contactskeap_smb_contactsid PK · custom fields → VARIANT or STRUCT columns
opportunitieskeap_opportunitiesid PK · linked to keap_smb_contacts
automationskeap_automationsid PK · linked to keap_smb_contacts
appointment workflowskeap_appointment_workflowsid PK · linked to keap_smb_contacts

FAQ

How does Datrise handle Keap'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 Keap 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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