Vtiger → Amazon Athena
AI-first ETL from Vtiger into Amazon Athena. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vtiger into Amazon Athena
Datrise syncs Vtiger's sales, support, and lifecycle workflows in a unified CRM model into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.
Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.
Ideal for serverless SQL over an S3 lake without a running warehouse.
Endpoints
Vtiger: Unified CRM for sales, help desk, and customer lifecycle workflows.
Amazon Athena: Serverless SQL over S3 data lake tables.
How Vtiger entities map to Amazon Athena
| Vtiger entity | Amazon Athena object | Notes |
|---|---|---|
| sales | vtiger_sales | id PK · custom fields → struct/map columns in Parquet |
| 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 Amazon Athena?
Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.
How does the Vtiger to Amazon Athena sync stay up to date?
It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.
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