Connect pike13 and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
Person Created
Person Updated
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of pike13 and TimescaleDB
Enable Integrations or automations with these events of pike13 and TimescaleDB
Triggers
Person Created
Triggers when a new person is added to your business.
Person Updated
Triggers when a person is updated in your business.
Actions
List Account
List all user's account details
List Businesses
List all Businesses
List Account People
List account people.
List Custom Fields
Lists the available custom fields.
List Event
Lists all the event.
List Event Occurrence
Lists all event occurrence.
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Frequently Asked Questions
To start, connect both your pike13 and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in pike13 triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how pike13 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 pike13 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 pike13 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 pike13
The mobile-friendly Pike13 platform allows class-based businesses like strength and conditioning gyms, yoga studios, and pilates studios to manage everything from clients and schedules to billing and payroll.
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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