Connect Sympla and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Event
New Orders
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Sympla and TimescaleDB
Enable Integrations or automations with these events of Sympla and TimescaleDB
Triggers
New Event
Fetch events that were created or updated since the last check.
New Orders
Fetch ticket orders for the selected Sympla event that were created since the last check.
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Frequently Asked Questions
To start, connect both your Sympla and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Sympla triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Sympla 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 Sympla 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 Sympla 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 Sympla
Sympla is a comprehensive platform for event management and ticketing, offering tools to organize, promote, and manage events of all sizes. It provides features for ticket sales, attendee management, and event promotion, making it ideal for event organizers and marketers.
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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