DatriseAI-first ETL

BoomTown to Amazon Redshift Integration

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

How Datrise loads BoomTown into Amazon Redshift

Datrise syncs BoomTown's leads, contacts, listings, opportunities, property alerts, and agent interactions into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

BoomTown: Real estate lead generation with predictive agent alerts.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How BoomTown entities map to Amazon Redshift

BoomTown entityAmazon Redshift objectNotes
leadsboomtown_leadsid PK · custom fields → SUPER columns
contactsboomtown_contactsid PK · linked to boomtown_leads
listingsboomtown_listingsid PK · linked to boomtown_leads
opportunitiesboomtown_opportunitiesid PK · linked to boomtown_leads

FAQ

How does Datrise handle BoomTown's custom fields in Amazon Redshift?

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

How does the BoomTown to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

Connect BoomTown to Amazon Redshift the easy way

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