DatriseAI-first ETL

Aws Cloudtrail MongoDB

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

How Datrise loads Aws Cloudtrail into MongoDB

Datrise syncs Aws Cloudtrail's records, events, and configuration objects into MongoDB as a collection per source entity. Flexible or custom fields land in native nested documents, and timestamps such as created, updated, and status changes are typed as BSON Date.

Sync is incremental: Datrise uses upserts by stable id with updateOne(upsert) on the source primary key, so re-runs update only what changed. Optional sharding on the entity id for large collections. Mongo has no fixed schema, so Datrise keeps field types consistent across documents to avoid mixed-type query surprises.

Ideal for document-oriented apps that want CRM data in their existing Mongo store.

Endpoints

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

MongoDB: Document database for flexible schemas.

How Aws Cloudtrail entities map to MongoDB

Aws Cloudtrail entityMongoDB objectNotes
recordsaws_cloudtrail_recordsid PK · custom fields → native nested documents
eventsaws_cloudtrail_eventsBSON Date events
configuration objectsaws_cloudtrail_configuration_objectsid PK · linked to aws_cloudtrail_records

FAQ

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

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

How does the Aws Cloudtrail to MongoDB sync stay up to date?

It runs incrementally — Datrise uses upserts by stable id with updateOne(upsert) on the source primary key.

Related pipelines

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