Connect ThriveCart and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
Product Is Purchased
Order Refunded Manual
Subscription Ends
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of ThriveCart and TimescaleDB
Enable Integrations or automations with these events of ThriveCart and TimescaleDB
Triggers
Product Is Purchased
Runs when product is purchased
Order Refunded Manual
Runs when order is refunded
Subscription Ends
Runs when subscription ends
Actions
Add New Student
Add a student to ThriveCart
Get Customer Details
Look up customer by email
Refund Transaction
Return payment to a customer
List Products
Get all products in your store
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Frequently Asked Questions
To start, connect both your ThriveCart and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in ThriveCart triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how ThriveCart 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 ThriveCart 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 ThriveCart 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 ThriveCart
Online shopping cart and checkout platform for selling products and managing orders.
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
