DatriseAI-first ETL

Snowflake to Snowflake Integration

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

How Datrise loads Snowflake into Snowflake

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses 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

Snowflake: Cloud data platform with separated storage and compute.

Snowflake: Cloud data warehouse with separated compute and storage.

How Snowflake entities map to Snowflake

Snowflake entitySnowflake objectNotes
databasessnowflake_databasesid PK · custom fields → VARIANT columns
schemassnowflake_schemasid PK · linked to snowflake_databases
tablessnowflake_tablesid PK · linked to snowflake_databases
viewssnowflake_viewsid PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake'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 Snowflake 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

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