Connect Modern Events Calendar and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Event
Modified Event
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Modern Events Calendar and TimescaleDB
Enable Integrations or automations with these events of Modern Events Calendar and TimescaleDB
Triggers
New Event
Triggers when a new event is created.
Modified Event
Triggers when an event is updated.
Actions
List Events
List all events.
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Frequently Asked Questions
To start, connect both your Modern Events Calendar and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Modern Events Calendar triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Modern Events Calendar 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 Modern Events Calendar 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 Modern Events Calendar 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 Modern Events Calendar
Modern Events Calendar is a responsive, mobile-friendly, FREE, and comprehensive events management plugin which is extremely user-friendly and well-designed for displaying the events calendar on the websites, ever easier.
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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