Connect Weekdone and TimescaleDB to Build Intelligent Automations
Enable Integrations or automations with these events of Weekdone and TimescaleDB
Enable Integrations or automations with these events of Weekdone and TimescaleDB
Triggers
New Objective
Triggers when a new objective is created in Weekdone.
New Item
Trigger when new item is created.
Actions
Create Objective
Creates a new objective in Weekdone.
Update Objective
Updates an existing objective in Weekdone.
Get All Items
Get all existing items
Create New Item
Create a New Item.
List Objectives
Get all Objectives
Update Item
Update item
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Frequently Asked Questions
To start, connect both your Weekdone and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Weekdone triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Weekdone 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 Weekdone 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 Weekdone 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 Weekdone
Weekdone is a comprehensive team management and productivity tool designed to help teams set goals, track progress, and achieve better results. It offers features like OKR (Objectives and Key Results) tracking, weekly check-ins, and real-time feedback to ensure everyone stays aligned and productive.
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About 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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