DatriseAI-first ETL

Deputy to Looker Integration

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

How Datrise loads Deputy into Looker

Datrise syncs Deputy's employees, shifts, timesheets, locations, leave requests, and pay rates into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Deputy: Workforce scheduling and timesheet management.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Deputy entities map to Looker

Deputy entityLooker objectNotes
employeesdeputy_employeesid PK · custom fields → flattened columns (nested fields expanded for modeling)
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 Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Deputy to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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