DatriseAI-first ETL

Deputy to Databricks SQL Warehouse Integration

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

How Datrise loads Deputy into Databricks SQL Warehouse

Datrise syncs Deputy's employees, shifts, timesheets, locations, leave requests, and pay rates 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

Deputy: Workforce scheduling and timesheet management.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Deputy entities map to Databricks SQL Warehouse

Deputy entityDatabricks SQL Warehouse objectNotes
employeesdeputy_employeesid PK · custom fields → VARIANT or STRUCT columns
shiftsdeputy_shiftsid PK · linked to deputy_employees
timesheetsdeputy_timesheetsid PK · linked to deputy_employees
locationsdeputy_locationsid PK · linked to deputy_employees

FAQ

How does Datrise handle Deputy'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 Deputy 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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