DatriseAI-first ETL

Datascope to Snowflake Integration

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

How Datrise loads Datascope into Snowflake

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

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Datascope: Mobile forms for field data capture.

Snowflake: Cloud data warehouse with separated compute and storage.

How Datascope entities map to Snowflake

Datascope entitySnowflake objectNotes
formsdatascope_formsid PK · custom fields → VARIANT 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 Snowflake?

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

How does the Datascope to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

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