Connect Once.to and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Links
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Once.to and TimescaleDB
Enable Integrations or automations with these events of Once.to and TimescaleDB
Triggers
New Links
Triggers when new Link is Created.
Actions
Create Link
Create a New Link
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Once.to and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Once.to triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Once.to 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 Once.to 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 Once.to 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 Once.to
Once is a platform designed to streamline and enhance your scheduling and booking processes. It offers a user-friendly interface for managing appointments, meetings, and events efficiently, ensuring that you never miss an important date or time. With Once, you can easily coordinate with clients, colleagues, and partners, making it an essential tool for businesses and individuals who value time management and organization.
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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