LookML to Snowflake Integration
AI-first ETL from LookML into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into Snowflake
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins 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
LookML: Semantic model definitions behind Looker content.
Snowflake: Cloud data warehouse with separated compute and storage.
How LookML entities map to Snowflake
| LookML entity | Snowflake object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → VARIANT columns |
| explores | lookml_explores | id PK · linked to lookml_models |
| views | lookml_views | id PK · linked to lookml_models |
| dimensions | lookml_dimensions | id PK · linked to lookml_models |
FAQ
How does Datrise handle LookML'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 LookML 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.
Related pipelines
More destinations for LookML
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