DatriseAI-first ETL

AWS CloudTrail to Looker Integration

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

How Datrise loads AWS CloudTrail into Looker

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How AWS CloudTrail entities map to Looker

AWS CloudTrail entityLooker objectNotes
API eventsaws_cloudtrail_api_eventsdate/time dimension 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_sourcesdate/time dimension columns events

FAQ

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

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

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

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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