DatriseAI-first ETL

PostgreSQL to Snowflake Integration

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

How Datrise loads PostgreSQL into Snowflake

Datrise syncs PostgreSQL's tables, rows, schemas, indexes, sequences, and replication slots 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

PostgreSQL: Self-managed PostgreSQL instances.

Snowflake: Cloud data warehouse with separated compute and storage.

How PostgreSQL entities map to Snowflake

PostgreSQL entitySnowflake objectNotes
tableshp_postgres_tablesid PK · custom fields → VARIANT columns
rowshp_postgres_rowsid PK · linked to hp_postgres_tables
schemashp_postgres_schemasid PK · linked to hp_postgres_tables
indexeshp_postgres_indexesid PK · linked to hp_postgres_tables

FAQ

How does Datrise handle PostgreSQL'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 PostgreSQL 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

Early access

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