DatriseAI-first ETL

Salesforce NPSP Looker

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

How Datrise loads Salesforce NPSP into Looker

Datrise syncs Salesforce NPSP'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

Salesforce NPSP: Nonprofit CRM for donors, campaigns, and stewardship.

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

How Salesforce NPSP entities map to Looker

Salesforce NPSP entityLooker objectNotes
contactssalesforce_npsp_contactsid PK · custom fields → flattened columns (nested fields expanded for modeling)
accountssalesforce_npsp_accountsid PK · linked to salesforce_npsp_contacts
dealssalesforce_npsp_dealsid PK · linked to salesforce_npsp_contacts
activitiessalesforce_npsp_activitiesdate/time dimension columns events

FAQ

How does Datrise handle Salesforce NPSP'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 Salesforce NPSP 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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