MONGODB ATLAS

Database Triggers

The easiest way to build real-time, event-driven applications. Automatically execute serverless functions to respond to specific events such as document inserts and updates. Or automate data management with tasks that run on your schedule.

Event-Driven Database Triggers

Atlas database triggers allow your database to listen for specific events — document inserts, updates, replaces and deletes — and respond in real-time with server-side logic. Use database triggers to easily implement complex data interactions with minimal data-handling code.

Update related documents

Automatically update information in a document when a related document changes.

Example: Update a separate document that logs customer purchase history after each new transaction.

Notify downstream services

Call a service API when a new document is inserted.

Example: Automatically trigger workflows that run new data through an image recognition service.

Propagate data to support mixed workloads

As documents are inserted, propagate relevant data across collections to support various workloads — e.g., analytics and business intelligence.

Example: As IoT data is loaded into a collection, automatically update aggregated information in a separate data mart and create notifications on any anomalies.

Data Integrity & Auditing

Database triggers provide an alternative way to check the integrity of data and audit data changes

Example: Each time a user updates her profile, create a record of the action in a separate collection.

Automatically update information in a document when a related document changes.

Example: Update a separate document that logs customer purchase history after each new transaction.

Scheduled Triggers

Atlas scheduled triggers allow you to automate the management of your data by executing recurring tasks that are triggered by the passage of time.

Scheduled data retrieval

Fetch data from an API on a recurring basis to keep your data relevant or to continually expand your dataset.

Scheduled data propagation

Move aggregate data to a data mart serving business analysts after a specified period of time has passed.

Scheduled data archival

Maximize operational efficiency while minimizing costs by automatically offloading historical data to an object store for archival storage.

Scheduled analytics workloads

Run longer-running analytics queries and generate reports during off-peak hours to reduce the risk of impacting performance.