Connect StoreHippo and TimescaleDB to Build Intelligent Automations
Enable Integrations or automations with these events of StoreHippo and TimescaleDB
Enable Integrations or automations with these events of StoreHippo and TimescaleDB
Triggers
New Product Created
Trigger when new product is created.
New User Enquiry
Trigger when a new user enquiry.
New User
Trigger when new user is created.
New Order
Triggers when there's a new order.
User Updated
Trigger when user is updated.
Actions
Add Enquiry
Creates a new enquiry in store-hippo system
Add Product
Adds a new product in storehippo system
Add User
Add a new User in Storehippo system
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Frequently Asked Questions
To start, connect both your StoreHippo and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in StoreHippo triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how StoreHippo 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 StoreHippo 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 StoreHippo 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 StoreHippo
StoreHippo is a comprehensive e-commerce platform designed to help businesses create, manage, and scale their online stores with ease. It offers a wide range of features including customizable themes, mobile-ready designs, multi-currency support, and integrated marketing tools.
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About 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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