DatriseAI-first ETL

AWS CloudTrail to Microsoft SQL Server Integration

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

How Datrise loads AWS CloudTrail into Microsoft SQL Server

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Microsoft SQL Server as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.

Ideal for Microsoft-stack analytics and Power BI Import models.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How AWS CloudTrail entities map to Microsoft SQL Server

AWS CloudTrail entityMicrosoft SQL Server objectNotes
API eventsaws_cloudtrail_api_eventsdatetime2 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_sourcesdatetime2 events

FAQ

How does Datrise handle AWS CloudTrail's custom fields in Microsoft SQL Server?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft SQL Server types.

How does the AWS CloudTrail to Microsoft SQL Server sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.

Related pipelines

Early access

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