Vincle to Sisense Integration
AI-first ETL from Vincle into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vincle into Sisense
Datrise syncs Vincle's customers, visits, orders, product assortments, promotions, and sales routes 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
Vincle: Field sales and trade execution for consumer goods.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Vincle entities map to Sisense
| Vincle entity | Sisense object | Notes |
|---|---|---|
| customers | vincle_customers | id PK · custom fields → flattened columns for the cube |
| visits | vincle_visits | id PK · linked to vincle_customers |
| orders | vincle_orders | id PK · linked to vincle_customers |
| product assortments | vincle_product_assortments | id PK · linked to vincle_customers |
FAQ
How does Datrise handle Vincle'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 Vincle to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
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