DatriseAI-first ETL

Sage CRM Looker

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 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

Sage CRM: Enterprise CRM for complex sales, service, and revenue operations.

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

How Sage CRM entities map to Looker

Sage CRM entityLooker objectNotes
contactssage_crm_contactsid PK · custom fields → flattened columns (nested fields expanded for modeling)
accountssage_crm_accountsid PK · linked to sage_crm_contacts
dealssage_crm_dealsid PK · linked to sage_crm_contacts
activitiessage_crm_activitiesdate/time dimension columns events

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

Early access

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