Chartmogul → Amazon Athena
AI-first ETL from Chartmogul into Amazon Athena. Governed entities, incremental sync, typed landing tables.
How Datrise loads Chartmogul into Amazon Athena
Datrise syncs Chartmogul's records, events, and configuration objects 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
Chartmogul: SaaS or API data source for analytics and warehouse sync.
Amazon Athena: Serverless SQL over S3 data lake tables.
How Chartmogul entities map to Amazon Athena
| Chartmogul entity | Amazon Athena object | Notes |
|---|---|---|
| records | chartmogul_records | id PK · custom fields → struct/map columns in Parquet |
| events | chartmogul_events | timestamp events |
| configuration objects | chartmogul_configuration_objects | id PK · linked to chartmogul_records |
FAQ
How does Datrise handle Chartmogul'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 Chartmogul 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.
Related pipelines
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Early access
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