
When this happens...

Automatically do this!
Enable Integrations or automations with these events of Chartbrew and MongoDB
Create a new Chartbrew team and set branding, report refresh, and export permissions.
Retrieve details for a specific Chartbrew team using its team ID.
Update a Chartbrew team's name and settings, including branding visibility and report refresh/export permissions.
Create a new Chartbrew project and its initial dashboard in the selected team.
Retrieve all projects for the specified Chartbrew team.
Retrieve a Chartbrew project's details by its project ID within the selected team.
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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Chartbrew and MongoDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Chartbrew triggers actions in MongoDB (or vice versa).
Absolutely. You can customize how Chartbrew 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 Chartbrew 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 Chartbrew 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.
Chartbrew is a powerful tool for creating and managing data visualizations. It allows users to connect to various data sources and build interactive charts and dashboards, making data analysis and presentation seamless and efficient.
Learn MoreMongoDB 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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