DatriseAI-first ETL

Typeform Google BigQuery

AI-first ETL from Typeform into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Typeform into Google BigQuery

Datrise syncs Typeform's forms, responses, questions, workspaces, and completion metrics into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Typeform: Conversational forms and response analytics.

Google BigQuery: Serverless analytics warehouse on GCP.

How Typeform entities map to Google BigQuery

Typeform entityGoogle BigQuery objectNotes
formstypeform_formsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
responsestypeform_responsesid PK · linked to typeform_forms
questionstypeform_questionsid PK · linked to typeform_forms
workspacestypeform_workspacesid PK · linked to typeform_forms

FAQ

How does Datrise handle Typeform's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Typeform to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

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