Clay to Snowflake Integration

How Datrise loads Clay into Snowflake

Datrise syncs Clay's tables, rows, enrichment runs, waterfalls, data sources, and exported lists 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

Clay: Data enrichment tables that build outbound lists.

Snowflake: Cloud data warehouse with separated compute and storage.

How Clay entities map to Snowflake

Clay entity	Snowflake object	Notes
tables	`clay_tables`	id PK · custom fields → VARIANT columns
rows	`clay_rows`	id PK · linked to clay_tables
enrichment runs	`clay_enrichment_runs`	id PK · linked to clay_tables
waterfalls	`clay_waterfalls`	id PK · linked to clay_tables

FAQ

How does Datrise handle Clay'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 Clay 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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