DatriseAI-first ETL

Datascope to Google BigQuery Integration

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

How Datrise loads Datascope into Google BigQuery

Datrise syncs Datascope's forms, submissions, users, locations, attachments, and answers 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

Datascope: Mobile forms for field data capture.

Google BigQuery: Serverless analytics warehouse on GCP.

How Datascope entities map to Google BigQuery

Datascope entityGoogle BigQuery objectNotes
formsdatascope_formsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
submissionsdatascope_submissionsid PK · linked to datascope_forms
usersdatascope_usersid PK · linked to datascope_forms
locationsdatascope_locationsid PK · linked to datascope_forms

FAQ

How does Datrise handle Datascope'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 Datascope 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

Early access

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