Connect workast and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Task Created
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of workast and TimescaleDB
Enable Integrations or automations with these events of workast and TimescaleDB
Triggers
New Task Created
Triggers when a new task is created in your Workast account.
Actions
Create a Task
create a task in workast app.
Create a Space
create a new space in workast.
Creates a Subtask
Creates a new subtask on a task.
Update Space
Update an existing space.
Create a Tag
Create a new tag
Add Tag
Add a tag to a task
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Frequently Asked Questions
To start, connect both your workast and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in workast triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how workast 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 workast 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 workast 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 workast
Workast is a productivity tool that helps teams capture tasks, manage projects and collaborate to get more work done.
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.
Learn More
