DatriseAI-first ETL

Moskit CRM Looker

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

How Datrise loads Moskit CRM into Looker

Datrise syncs Moskit 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

Moskit 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 Moskit CRM entities map to Looker

Moskit CRM entityLooker objectNotes
contactsmoskit_contactsid PK · custom fields → flattened columns (nested fields expanded for modeling)
accountsmoskit_accountsid PK · linked to moskit_contacts
dealsmoskit_dealsid PK · linked to moskit_contacts
activitiesmoskit_activitiesdate/time dimension columns events

FAQ

How does Datrise handle Moskit 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 Moskit 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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