DatriseAI-first ETL

AWS CloudTrail to Apache Superset Integration

AI-first ETL from AWS CloudTrail into Apache Superset. Governed entities, incremental sync, typed landing tables.

How Datrise loads AWS CloudTrail into Apache Superset

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Apache Superset as governed SQL tables Superset queries directly. Flexible or custom fields land in flattened columns for the explore UI, and timestamps such as created, updated, and status changes are typed as temporal columns for time-series charts.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned tables to keep dashboards responsive. Superset charts run live SQL, so Datrise lands query-friendly, indexed tables rather than wide raw payloads.

Ideal for open-source dashboards over your own database.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Apache Superset: Open-source BI for SQL exploration, charts, and dashboard publishing.

How AWS CloudTrail entities map to Apache Superset

AWS CloudTrail entityApache Superset objectNotes
API eventsaws_cloudtrail_api_eventstemporal columns for time-series charts events
identitiesaws_cloudtrail_identitiesid PK · linked to aws_cloudtrail_api_events
resourcesaws_cloudtrail_resourcesid PK · linked to aws_cloudtrail_api_events
event sourcesaws_cloudtrail_event_sourcestemporal columns for time-series charts events

FAQ

How does Datrise handle AWS CloudTrail's custom fields in Apache Superset?

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

How does the AWS CloudTrail to Apache Superset sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

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