Connect Orderspace and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Customer
New Dispatch
New Invoice
New Order
New Payment
New Product
Updated Customer
Updated Inventory Level
Updated Order
Delete Customer
Deleted Invoice
Updated Product
Deleted Product
Deleted Order
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Orderspace and TimescaleDB
Enable Integrations or automations with these events of Orderspace and TimescaleDB
Triggers
New Customer
Triggers when a new customer is created.
New Dispatch
Triggers when a new dispatch is created.
New Invoice
Triggers when a new invoice is created.
New Order
Triggers when a new order is created.
New Payment
Triggers when a new payment is created.
New Product
Triggers when a new product is created.
Actions
Create Customer
Creating a new customer
Create a Price List
Create a new price list.
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Frequently Asked Questions
To start, connect both your Orderspace and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Orderspace triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Orderspace 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 Orderspace 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 Orderspace 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 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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