Connect Jobber and MongoDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Client Is Created
Client Is Updated
Client Is Deleted
New Invoice Is Created
Invoice Updated
New Job Is Created
Job Is Closed
Job Is Updated
New Quote Is Created
Quote Approved
Quote Sent
New Request Created
Choose an Action
Automatically do this!
- Request a new Action for MongoDB
Enable Integrations or automations with these events of Jobber and MongoDB
Enable Integrations or automations with these events of Jobber and MongoDB
Triggers
New Client Is Created
Runs when new client is created
Client Is Updated
Runs when client is updated
Client Is Deleted
Runs when Client Is Deleted
New Invoice Is Created
Runs when New Invoice Is Created
Invoice Updated
Runs when invoice is updated
New Job Is Created
Runs when new job is created
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Frequently Asked Questions
To start, connect both your Jobber and MongoDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Jobber triggers actions in MongoDB (or vice versa).
Absolutely. You can customize how Jobber data is recorded in MongoDB. This includes choosing which data fields go into which fields of MongoDB, setting up custom formats, and filtering out unwanted information.
The data sync between Jobber and MongoDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Jobber and MongoDB. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Jobber
Jobber is the command centre for home service businesses. Our easy-to-use app powers sales, operations, and customer service—all in one place.
Learn MoreAbout MongoDB
MongoDB is a leading NoSQL database platform that provides a flexible and scalable solution for managing large volumes of data. It is designed to handle unstructured data and offers high performance, high availability, and easy scalability.
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