Connect Teambook and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Project
New User
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Teambook and TimescaleDB
Enable Integrations or automations with these events of Teambook and TimescaleDB
Triggers
New Project
Triggers when a new project is created.
New User
Actions
Create User
Create a teambook user.
List Project
List all projects.
List Users
List all users.
List Actual Log
List all Actual Log.
List Task
List all task
List Team
List all team.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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- InstagramInstagram CaptionGenerate ImagePublish Photo with TaggingAdd Step
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Frequently Asked Questions
To start, connect both your Teambook and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Teambook triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Teambook 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 Teambook 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 Teambook 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 Teambook
TeamBook is an easy-to-use app that helps your team collaborate, manage projects, and stay organized. Perfect for remote or in-office teams, TeamBook makes teamwork simple and effective.
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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