DatriseAI-first ETL

Vtiger Amazon QuickSight

AI-first ETL from Vtiger into Amazon QuickSight. Governed entities, incremental sync, typed landing tables.

How Datrise loads Vtiger into Amazon QuickSight

Datrise syncs Vtiger's sales, support, and lifecycle workflows in a unified CRM model into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Vtiger: Unified CRM for sales, help desk, and customer lifecycle workflows.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Vtiger entities map to Amazon QuickSight

Vtiger entityAmazon QuickSight objectNotes
salesvtiger_salesid PK · custom fields → flattened columns for analyses
supportvtiger_supportid PK · linked to vtiger_sales
lifecycle workflows in a unified CRM modelvtiger_lifecycle_workflows_in_a_unified_crm_modelid PK · linked to vtiger_sales

FAQ

How does Datrise handle Vtiger's custom fields in Amazon QuickSight?

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

How does the Vtiger to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

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