DatriseAI-first ETL

SurveySparrow to Looker Integration

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

How Datrise loads SurveySparrow into Looker

Datrise syncs SurveySparrow's surveys, responses, questions, contacts, NPS scores, and share links 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

SurveySparrow: Conversational surveys and feedback workflows.

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

How SurveySparrow entities map to Looker

SurveySparrow entityLooker objectNotes
surveyssurveysparrow_surveysid PK · custom fields → flattened columns (nested fields expanded for modeling)
responsessurveysparrow_responsesid PK · linked to surveysparrow_surveys
questionssurveysparrow_questionsid PK · linked to surveysparrow_surveys
contactssurveysparrow_contactsid PK · linked to surveysparrow_surveys

FAQ

How does Datrise handle SurveySparrow'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 SurveySparrow 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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