DatriseAI-first ETL

AWS CloudTrail to Snowflake Integration

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

How Datrise loads AWS CloudTrail into Snowflake

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Snowflake: Cloud data warehouse with separated compute and storage.

How AWS CloudTrail entities map to Snowflake

AWS CloudTrail entitySnowflake objectNotes
API eventsaws_cloudtrail_api_eventsTIMESTAMP_TZ 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_sourcesTIMESTAMP_TZ events

FAQ

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

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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