DatriseAI-first ETL

Datascope to Databricks SQL Warehouse Integration

AI-first ETL from Datascope into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Datascope into Databricks SQL Warehouse

Datrise syncs Datascope's forms, submissions, users, locations, attachments, and answers into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Datascope: Mobile forms for field data capture.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Datascope entities map to Databricks SQL Warehouse

Datascope entityDatabricks SQL Warehouse objectNotes
formsdatascope_formsid PK · custom fields → VARIANT or 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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Datascope to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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