DatriseAI-first ETL

Bullhorn Mode

AI-first ETL from Bullhorn into Mode. Governed entities, incremental sync, typed landing tables.

How Datrise loads Bullhorn into Mode

Datrise syncs Bullhorn's contacts, accounts, deals, activities, and lifecycle events into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

Bullhorn: Recruiting CRM/ATS for candidates, pipelines, and placements.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Bullhorn entities map to Mode

Bullhorn entityMode objectNotes
contactsbullhorn_contactsid PK · custom fields → flattened columns for SQL and notebooks
accountsbullhorn_accountsid PK · linked to bullhorn_contacts
dealsbullhorn_dealsid PK · linked to bullhorn_contacts
activitiesbullhorn_activitiestemporal columns events

FAQ

How does Datrise handle Bullhorn's custom fields in Mode?

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

How does the Bullhorn to Mode sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

Connect Bullhorn to Mode the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.