DatriseAI-first ETL

Crelate Databricks SQL Warehouse

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

How Datrise loads Crelate into Databricks SQL Warehouse

Datrise syncs Crelate's contacts, accounts, deals, activities, and lifecycle 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

Crelate: Recruiting CRM/ATS for candidates, pipelines, and placements.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Crelate entities map to Databricks SQL Warehouse

Crelate entityDatabricks SQL Warehouse objectNotes
contactscrelate_contactsid PK · custom fields → VARIANT or STRUCT columns
accountscrelate_accountsid PK · linked to crelate_contacts
dealscrelate_dealsid PK · linked to crelate_contacts
activitiescrelate_activitiesTIMESTAMP events

FAQ

How does Datrise handle Crelate'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 Crelate 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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