Connect Everhour and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Everhour
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Everhour and TimescaleDB
Enable Integrations or automations with these events of Everhour and TimescaleDB
Actions
Create Task
Creates a new task in Everhour.
Create Project
Creates a new project.
Add Time
Adds your time to a task.
Create Section
Сreates section (column) in a project.
Start Timer
Starts a new timer for a task.
Stop Timer
Stops the current timer.
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Frequently Asked Questions
To start, connect both your Everhour and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Everhour triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Everhour 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 Everhour 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 Everhour 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 Everhour
Everhour is a time tracking and project management tool designed to help teams and businesses efficiently manage their time and resources. It integrates seamlessly with popular project management platforms, allowing users to track time, set budgets, and generate insightful reports to optimize productivity and project outcomes.
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
