DatriseAI-first ETL

AWS CloudTrail to Sisense Integration

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

How Datrise loads AWS CloudTrail into Sisense

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Sisense: Analytics platform with elastic data models and embedded analytics.

How AWS CloudTrail entities map to Sisense

AWS CloudTrail entitySisense objectNotes
API eventsaws_cloudtrail_api_eventsdate/time fields 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 fields events

FAQ

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

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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