Connect RepairShopr and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for RepairShopr
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of RepairShopr and TimescaleDB
Enable Integrations or automations with these events of RepairShopr and TimescaleDB
Actions
List Appointment Type
Returns the list of appointment types
List All Custom Assets
Returns the list of all custom assets
List All Contacts
Returns the list of all contacts
List All Customers
Returns the list of all customers
List All Estimates
Returns the list of all estimates
List All Invoices
Returns the list of all invoices
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Frequently Asked Questions
To start, connect both your RepairShopr and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in RepairShopr triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how RepairShopr 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 RepairShopr 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 RepairShopr 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 RepairShopr
RepairShopr is a platform to manage a service or repair business with job tracking (ticketing) and an emphasis on great communication.
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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