BoomTown to Birst Integration
AI-first ETL from BoomTown into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads BoomTown into Birst
Datrise syncs BoomTown's leads, contacts, listings, opportunities, property alerts, and agent interactions into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
BoomTown: Real estate lead generation with predictive agent alerts.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How BoomTown entities map to Birst
| BoomTown entity | Birst object | Notes |
|---|---|---|
| leads | boomtown_leads | id PK · custom fields → flattened columns |
| contacts | boomtown_contacts | id PK · linked to boomtown_leads |
| listings | boomtown_listings | id PK · linked to boomtown_leads |
| opportunities | boomtown_opportunities | id PK · linked to boomtown_leads |
FAQ
How does Datrise handle BoomTown's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the BoomTown to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
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Early access
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