DatriseAI-first ETL

Pipedrive Databricks SQL Warehouse

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

How Datrise loads Pipedrive into Databricks SQL Warehouse

Datrise syncs Pipedrive's deals, persons, organizations, activities, and stage movement analytics 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

Pipedrive: Pipeline-first CRM for sales teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Pipedrive entities map to Databricks SQL Warehouse

Pipedrive entityDatabricks SQL Warehouse objectNotes
dealspipedrive_dealsid PK · custom fields → VARIANT or STRUCT columns
personspipedrive_personsid PK · linked to pipedrive_deals
organizationspipedrive_organizationsid PK · linked to pipedrive_deals
activitiespipedrive_activitiesTIMESTAMP events

FAQ

How does Datrise handle Pipedrive'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 Pipedrive 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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