Connect DragApp and Google Cloud Bigtable to Build Intelligent Automations
Choose a Trigger
When this happens...
New Task Cards
Choose an Action
Automatically do this!
- Request a new Action for Google Cloud Bigtable
Enable Integrations or automations with these events of DragApp and Google Cloud Bigtable
Enable Integrations or automations with these events of DragApp and Google Cloud Bigtable
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 Google Cloud Bigtable accounts to viaSocket. Once connected, you can set up a workflow where an event in DragApp triggers actions in Google Cloud Bigtable (or vice versa).
Absolutely. You can customize how DragApp data is recorded in Google Cloud Bigtable. This includes choosing which data fields go into which fields of Google Cloud Bigtable, setting up custom formats, and filtering out unwanted information.
The data sync between DragApp and Google Cloud Bigtable 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 Google Cloud Bigtable. 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 Google Cloud Bigtable
Google Cloud Bigtable is a fully managed, scalable NoSQL database service designed for large analytical and operational workloads. It is ideal for applications that require high throughput and low latency, such as IoT, user analytics, and financial data analysis.
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