DatriseAI-first ETL

AWS CloudTrail to Google BigQuery Integration

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

How Datrise loads AWS CloudTrail into Google BigQuery

Datrise syncs AWS CloudTrail's API events, identities, resources, event sources, error codes, and request parameters into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

AWS CloudTrail: AWS account activity and API audit logging.

Google BigQuery: Serverless analytics warehouse on GCP.

How AWS CloudTrail entities map to Google BigQuery

AWS CloudTrail entityGoogle BigQuery objectNotes
API eventsaws_cloudtrail_api_eventsTIMESTAMP 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 events

FAQ

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

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the AWS CloudTrail to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

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