Apollo → Snowflake
AI-first ETL from Apollo into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Snowflake
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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
Apollo: Sales intelligence and engagement platform with account-level activity.
Snowflake: Cloud data warehouse with separated compute and storage.
How Apollo entities map to Snowflake
| Apollo entity | Snowflake object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → VARIANT columns |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | TIMESTAMP_TZ events |
FAQ
How does Datrise handle Apollo'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 Apollo 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 Apollo
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