DatriseAI-first ETL

Aws Cloudtrail Amazon Redshift

AI-first ETL from Aws Cloudtrail into Amazon Redshift. Governed entities, incremental sync, typed landing tables.

How Datrise loads Aws Cloudtrail into Amazon Redshift

Datrise syncs Aws Cloudtrail's records, events, and configuration objects into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Aws Cloudtrail: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Aws Cloudtrail entities map to Amazon Redshift

Aws Cloudtrail entityAmazon Redshift objectNotes
recordsaws_cloudtrail_recordsid PK · custom fields → SUPER columns
eventsaws_cloudtrail_eventsTIMESTAMPTZ events
configuration objectsaws_cloudtrail_configuration_objectsid PK · linked to aws_cloudtrail_records

FAQ

How does Datrise handle Aws Cloudtrail's custom fields in Amazon Redshift?

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

How does the Aws Cloudtrail to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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