Connect Desklog.io and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Desklog.io
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Desklog.io and TimescaleDB
Enable Integrations or automations with these events of Desklog.io and TimescaleDB
Actions
Create Task
Creates a new task in Desklog.io.
Update Task
Updates an existing task in Desklog.io.
Create Project
Creates a Project.
Create Client
Creates a Client.
Create Tag
Creates a Tag.
Update Tag
Update an existing tag.
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Frequently Asked Questions
To start, connect both your Desklog.io and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Desklog.io triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Desklog.io 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 Desklog.io 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 Desklog.io 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 Desklog.io
Desklog.io is a comprehensive project management and time tracking software designed to enhance productivity and streamline workflow processes. It offers features such as task management, employee monitoring, and automated timesheets, making it an ideal solution for businesses looking to optimize their project management and team collaboration efforts.
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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