DatriseAI-first ETL

Vincle to Looker Integration

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

How Datrise loads Vincle into Looker

Datrise syncs Vincle's customers, visits, orders, product assortments, promotions, and sales routes 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

Vincle: Field sales and trade execution for consumer goods.

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

How Vincle entities map to Looker

Vincle entityLooker objectNotes
customersvincle_customersid PK · custom fields → flattened columns (nested fields expanded for modeling)
visitsvincle_visitsid PK · linked to vincle_customers
ordersvincle_ordersid PK · linked to vincle_customers
product assortmentsvincle_product_assortmentsid PK · linked to vincle_customers

FAQ

How does Datrise handle Vincle'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 Vincle 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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