Omie CRM → Looker
AI-first ETL from Omie CRM into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Omie CRM into Looker
Datrise syncs Omie CRM's contacts, accounts, deals, activities, and lifecycle events 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
Omie CRM: CRM widely used in Latin America for sales pipeline and customer ops.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Omie CRM entities map to Looker
| Omie CRM entity | Looker object | Notes |
|---|---|---|
| contacts | omie_contacts | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| accounts | omie_accounts | id PK · linked to omie_contacts |
| deals | omie_deals | id PK · linked to omie_contacts |
| activities | omie_activities | date/time dimension columns events |
FAQ
How does Datrise handle Omie CRM'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 Omie CRM 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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