DatriseAI-first ETL

AWS CloudTrail to Mode Integration

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

How Datrise loads AWS CloudTrail into Mode

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How AWS CloudTrail entities map to Mode

AWS CloudTrail entityMode objectNotes
API eventsaws_cloudtrail_api_eventstemporal columns 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 events

FAQ

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

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

How does the AWS CloudTrail to Mode sync stay up to date?

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

Related pipelines

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