DatriseAI-first ETL

SurveyMonkey Looker

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

How Datrise loads SurveyMonkey into Looker

Datrise syncs SurveyMonkey's surveys, questions, responses, collectors, and quotas 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

SurveyMonkey: Survey creation and response analytics.

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

How SurveyMonkey entities map to Looker

SurveyMonkey entityLooker objectNotes
surveyssurveymonkey_surveysid PK · custom fields → flattened columns (nested fields expanded for modeling)
questionssurveymonkey_questionsid PK · linked to surveymonkey_surveys
responsessurveymonkey_responsesid PK · linked to surveymonkey_surveys
collectorssurveymonkey_collectorsid PK · linked to surveymonkey_surveys

FAQ

How does Datrise handle SurveyMonkey'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 SurveyMonkey 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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