Vitally to Amazon Athena Integration
AI-first ETL from Vitally into Amazon Athena. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vitally into Amazon Athena
Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage 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
Vitally: Customer success health and account analytics.
Amazon Athena: Serverless SQL over S3 data lake tables.
How Vitally entities map to Amazon Athena
| Vitally entity | Amazon Athena object | Notes |
|---|---|---|
| accounts | vitally_accounts | id PK · custom fields → struct/map columns in Parquet |
| users | vitally_users | id PK · linked to vitally_accounts |
| health scores | vitally_health_scores | id PK · linked to vitally_accounts |
| tasks | vitally_tasks | id PK · linked to vitally_accounts |
FAQ
How does Datrise handle Vitally'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 Vitally 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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Early access
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