Apollo → CSV Files
AI-first ETL from Apollo into CSV Files. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into CSV Files
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.
Ideal for portable hand-off into any tool that ingests delimited files.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
CSV Files: Flat-file destination for exports and lightweight data sharing.
How Apollo entities map to CSV Files
| Apollo entity | CSV Files object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → JSON-encoded strings for nested fields |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle Apollo's custom fields in CSV Files?
Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.
How does the Apollo to CSV Files sync stay up to date?
It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.
Related pipelines
More destinations for Apollo
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