Connect Qminder and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
Add visitor to queue
visitor checks in
Mark visitor as served
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Qminder and TimescaleDB
Enable Integrations or automations with these events of Qminder and TimescaleDB
Triggers
Add visitor to queue
Triggers when a new visitor is added.
visitor checks in
runs when visitor checks in
Mark visitor as served
Set a visitor's status to 'served' in Qminder to update the queue and record service completion.
Actions
Add visitor to line
Add a visitor to the selected line so they join the location's queue.
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Frequently Asked Questions
To start, connect both your Qminder and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Qminder triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Qminder 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 Qminder 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 Qminder 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 Qminder
Qminder is a queue management system designed to streamline customer service and improve the waiting experience. It helps businesses manage queues efficiently, providing real-time data and insights to enhance customer satisfaction.
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
