Salesflare → Mode
AI-first ETL from Salesflare into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesflare into Mode
Datrise syncs Salesflare's B2B account intelligence, interactions, and automated relationship timelines 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
Salesflare: B2B CRM focused on automation and relationship timeline data.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Salesflare entities map to Mode
| Salesflare entity | Mode object | Notes |
|---|---|---|
| B2B account intelligence | salesflare_b2b_account_intelligence | id PK · custom fields → flattened columns for SQL and notebooks |
| interactions | salesflare_interactions | id PK · linked to salesflare_b2b_account_intelligence |
| automated relationship timelines | salesflare_automated_relationship_timelines | temporal columns events |
FAQ
How does Datrise handle Salesflare'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 Salesflare to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
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