DatriseAI-first ETL

HubSpot CRM Databricks SQL Warehouse

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

How Datrise loads HubSpot CRM into Databricks SQL Warehouse

Datrise syncs HubSpot CRM's contacts, companies, deals, lifecycle stages, and engagement events 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

HubSpot CRM: Inbound CRM with marketing, sales, and service hubs.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How HubSpot CRM entities map to Databricks SQL Warehouse

HubSpot CRM entityDatabricks SQL Warehouse objectNotes
contactshubspot_contactsid PK · custom fields → VARIANT or STRUCT columns
companieshubspot_companiesid PK · linked to hubspot_contacts
dealshubspot_dealsid PK · linked to hubspot_contacts
lifecycle stageshubspot_lifecycle_stagesid PK · linked to hubspot_contacts

FAQ

How does Datrise handle HubSpot CRM'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 HubSpot CRM 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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