Connect Detrack and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Detrack
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Detrack and TimescaleDB
Enable Integrations or automations with these events of Detrack and TimescaleDB
Actions
Create Job
Creates a Job in the Detrack Dashboard.
List Jobs
List all jobs
Create Batch
Create multiple delivery or collection jobs in Detrack as a single batch.
Update Job
Update one or more Detrack jobs (DO numbers). Modify job date, type, address, contact, phone, notification email, tracking number, and items — any fields left blank remain unchanged.
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Frequently Asked Questions
To start, connect both your Detrack and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Detrack triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Detrack 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 Detrack 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 Detrack 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 Detrack
Detrack is a live delivery tracking system that offers a robust Proof of Delivery system to last-mile couriers and logistics.
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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