WorkRamp to Snowflake Integration
AI-first ETL from WorkRamp into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads WorkRamp into Snowflake
Datrise syncs WorkRamp's courses, learners, enrollments, completions, assignments, and certifications 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
WorkRamp: Employee training, courses, and completion tracking.
Snowflake: Cloud data warehouse with separated compute and storage.
How WorkRamp entities map to Snowflake
| WorkRamp entity | Snowflake object | Notes |
|---|---|---|
| courses | workramp_courses | id PK · custom fields → VARIANT columns |
| learners | workramp_learners | id PK · linked to workramp_courses |
| enrollments | workramp_enrollments | id PK · linked to workramp_courses |
| completions | workramp_completions | id PK · linked to workramp_courses |
FAQ
How does Datrise handle WorkRamp'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 WorkRamp 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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