DatriseAI-first ETL

BoomTown PostgreSQL

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

How Datrise loads BoomTown into PostgreSQL

Datrise syncs BoomTown's contacts, accounts, deals, activities, and lifecycle events into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

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

PostgreSQL: Open-source relational database with strong SQL and extensions.

How BoomTown entities map to PostgreSQL

BoomTown entityPostgreSQL objectNotes
contactsboomtown_contactsid PK · custom fields → jsonb columns
accountsboomtown_accountsid PK · linked to boomtown_contacts
dealsboomtown_dealsid PK · linked to boomtown_contacts
activitiesboomtown_activitiestimestamptz events

FAQ

How does Datrise handle BoomTown's custom fields in PostgreSQL?

Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.

How does the BoomTown to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

Early access

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