Vtiger → Sisense
AI-first ETL from Vtiger into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vtiger into Sisense
Datrise syncs Vtiger's sales, support, and lifecycle workflows in a unified CRM model 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
Vtiger: Unified CRM for sales, help desk, and customer lifecycle workflows.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Vtiger entities map to Sisense
| Vtiger entity | Sisense object | Notes |
|---|---|---|
| sales | vtiger_sales | id PK · custom fields → flattened columns for the cube |
| support | vtiger_support | id PK · linked to vtiger_sales |
| lifecycle workflows in a unified CRM model | vtiger_lifecycle_workflows_in_a_unified_crm_model | id PK · linked to vtiger_sales |
FAQ
How does Datrise handle Vtiger'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 Vtiger to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
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