Salesflare → Sisense
AI-first ETL from Salesflare into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesflare into Sisense
Datrise syncs Salesflare's B2B account intelligence, interactions, and automated relationship timelines into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
Salesflare: B2B CRM focused on automation and relationship timeline data.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Salesflare entities map to Sisense
| Salesflare entity | Sisense object | Notes |
|---|---|---|
| B2B account intelligence | salesflare_b2b_account_intelligence | id PK · custom fields → flattened columns for the cube |
| interactions | salesflare_interactions | id PK · linked to salesflare_b2b_account_intelligence |
| automated relationship timelines | salesflare_automated_relationship_timelines | date/time fields events |
FAQ
How does Datrise handle Salesflare's custom fields in Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the Salesflare to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
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