Connect DragApp and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Task Cards
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of DragApp and TimescaleDB
Enable Integrations or automations with these events of DragApp and TimescaleDB
Triggers
New Task Cards
Retrieves task cards created in the selected board column within the specified time window.
Actions
Create Card
Add a new card to a Drag board using title and column; optionally include note, comment, tag, assignee, and subtasks.
Add Card Comment
Add a comment to the card (found by title) in the selected board and column.
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Frequently Asked Questions
To start, connect both your DragApp and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in DragApp triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how DragApp data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between DragApp and TimescaleDB 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 DragApp and TimescaleDB. 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 DragApp
Drag turns Gmail into a Team Workspace to manage Helpdesk, CRM, tasks and other workflows from your inbox. Built for Google Workspace users.
Learn MoreAbout TimescaleDB
TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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