DatriseAI-first ETL

AWS CloudTrail to Spotfire Integration

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

How Datrise loads AWS CloudTrail into Spotfire

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How AWS CloudTrail entities map to Spotfire

AWS CloudTrail entitySpotfire objectNotes
API eventsaws_cloudtrail_api_eventsdate/time 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 columns events

FAQ

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

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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