Close → Snowflake
AI-first ETL from Close into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Close into Snowflake
Datrise syncs Close's leads, opportunities, calls, SMS events, and sequence performance 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
Close: Inside-sales CRM with calling and sequences.
Snowflake: Cloud data warehouse with separated compute and storage.
How Close entities map to Snowflake
| Close entity | Snowflake object | Notes |
|---|---|---|
| leads | close_leads | id PK · custom fields → VARIANT columns |
| opportunities | close_opportunities | id PK · linked to close_leads |
| calls | close_calls | id PK · linked to close_leads |
| SMS events | close_sms_events | TIMESTAMP_TZ events |
FAQ
How does Datrise handle Close'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 Close 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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