DatriseAI-first ETL

Vtiger Google BigQuery

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

How Datrise loads Vtiger into Google BigQuery

Datrise syncs Vtiger's sales, support, and lifecycle workflows in a unified CRM model into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Vtiger entities map to Google BigQuery

Vtiger entityGoogle BigQuery objectNotes
salesvtiger_salesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Vtiger to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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