Connect Starshipit and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Order
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Starshipit and TimescaleDB
Enable Integrations or automations with these events of Starshipit and TimescaleDB
Triggers
New Order
Triggers when a new order is created.
Actions
Create a Order
Create an outbound order.
List Shipped Orders
Retrieve details of orders that have been shipped.
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Frequently Asked Questions
To start, connect both your Starshipit and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Starshipit triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Starshipit 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 Starshipit 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 Starshipit 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 Starshipit
Starshipit is an integrated shipping and tracking software that helps businesses automate their shipping processes. The app allows users to manage orders, print shipping labels, and provide real-time tracking updates to customers, enhancing the efficiency of e-commerce operations.
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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