DatriseAI-first ETL

Salesforce CSV Files

AI-first ETL from Salesforce into CSV Files. Governed entities, incremental sync, typed landing tables.

How Datrise loads Salesforce into CSV Files

Datrise syncs Salesforce's accounts, opportunities, contacts, tasks, and pipeline stage history 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

Salesforce: Enterprise CRM and Customer 360 platform.

CSV Files: Flat-file destination for exports and lightweight data sharing.

How Salesforce entities map to CSV Files

Salesforce entityCSV Files objectNotes
accountssalesforce_accountsid PK · custom fields → JSON-encoded strings for nested fields
opportunitiessalesforce_opportunitiesid PK · linked to salesforce_accounts
contactssalesforce_contactsid PK · linked to salesforce_accounts
taskssalesforce_tasksid PK · linked to salesforce_accounts

FAQ

How does Datrise handle Salesforce'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 Salesforce 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

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