DatriseAI-first ETL

Sage CRM to Looker Integration

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

How Datrise loads Sage CRM into Looker

Datrise syncs Sage CRM's persons, companies, opportunities, cases, communications, and leads 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

Sage CRM: CRM tied to Sage accounting for mid-market operations.

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

How Sage CRM entities map to Looker

Sage CRM entityLooker objectNotes
personssage_crm_personsid PK · custom fields → flattened columns (nested fields expanded for modeling)
companiessage_crm_companiesid PK · linked to sage_crm_persons
opportunitiessage_crm_opportunitiesid PK · linked to sage_crm_persons
casessage_crm_casesid PK · linked to sage_crm_persons

FAQ

How does Datrise handle Sage 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 Sage 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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