Connect Nimble Links and TimescaleDB to Build Intelligent Automations
Enable Integrations or automations with these events of Nimble Links and TimescaleDB
Enable Integrations or automations with these events of Nimble Links and TimescaleDB
Actions
Create short link
Shorten a URL into a shareable short link.
Create Random Redirect Link
Create a short link that sends visitors to a random destination from the list of destination.
Create Redirect Evenly Link
Creates a link that distributes traffic evenly between multiple destination URLs.
Create Password-Protected Link
Creates a link that requires a password to access before redirecting to the destination URL.
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Frequently Asked Questions
To start, connect both your Nimble Links and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Nimble Links triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Nimble Links 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 Nimble Links 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 Nimble Links 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 Nimble Links
Nimble Links is a powerful tool designed to streamline your URL management and enhance your online presence. With its intuitive interface, you can easily shorten, track, and manage your links, making it an essential tool for marketers and businesses looking to optimize their digital strategies.
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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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