DatriseAI-first ETL

Listrak Looker

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

How Datrise loads Listrak into Looker

Datrise syncs Listrak's records, events, and configuration objects 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

Listrak: SaaS or API data source for analytics and warehouse sync.

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

How Listrak entities map to Looker

Listrak entityLooker objectNotes
recordslistrak_recordsid PK · custom fields → flattened columns (nested fields expanded for modeling)
eventslistrak_eventsdate/time dimension columns events
configuration objectslistrak_configuration_objectsid PK · linked to listrak_records

FAQ

How does Datrise handle Listrak'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 Listrak 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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