DatriseAI-first ETL

BoomTown CSV Files

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

How Datrise loads BoomTown into CSV Files

Datrise syncs BoomTown's contacts, accounts, deals, activities, and lifecycle events 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

BoomTown: Real estate CRM for leads, listings, and agent follow-up.

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

How BoomTown entities map to CSV Files

BoomTown entityCSV Files objectNotes
contactsboomtown_contactsid PK · custom fields → JSON-encoded strings for nested fields
accountsboomtown_accountsid PK · linked to boomtown_contacts
dealsboomtown_dealsid PK · linked to boomtown_contacts
activitiesboomtown_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle BoomTown'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 BoomTown 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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