Redshift to Snowflake Integration
AI-first ETL from Redshift into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Redshift into Snowflake
Datrise syncs Redshift's tables, rows, schemas, sort keys, distribution keys, and query history 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
Redshift: Columnar cloud data warehouse on AWS.
Snowflake: Cloud data warehouse with separated compute and storage.
How Redshift entities map to Snowflake
| Redshift entity | Snowflake object | Notes |
|---|---|---|
| tables | redshift_tables | id PK · custom fields → VARIANT columns |
| rows | redshift_rows | id PK · linked to redshift_tables |
| schemas | redshift_schemas | id PK · linked to redshift_tables |
| sort keys | redshift_sort_keys | id PK · linked to redshift_tables |
FAQ
How does Datrise handle Redshift'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 Redshift 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 Redshift
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