DatriseAI-first ETL

Qualaroo to Looker Integration

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

How Datrise loads Qualaroo into Looker

Datrise syncs Qualaroo's surveys, responses, questions, respondents, nudges, and sentiment scores 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

Qualaroo: On-site surveys and user sentiment capture.

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

How Qualaroo entities map to Looker

Qualaroo entityLooker objectNotes
surveysqualaroo_surveysid PK · custom fields → flattened columns (nested fields expanded for modeling)
responsesqualaroo_responsesid PK · linked to qualaroo_surveys
questionsqualaroo_questionsid PK · linked to qualaroo_surveys
respondentsqualaroo_respondentsid PK · linked to qualaroo_surveys

FAQ

How does Datrise handle Qualaroo'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 Qualaroo 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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