Connect VATlayer and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for VATlayer
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of VATlayer and TimescaleDB
Enable Integrations or automations with these events of VATlayer and TimescaleDB
Actions
Validate VAT number
Verify a VAT number and get its country and validation status
Get VAT rate
Get the VAT rate for a country or VAT number.
Get VAT rates lists
Retrieve current VAT rates (standard and reduced) for countries.
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Frequently Asked Questions
To start, connect both your VATlayer and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in VATlayer triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how VATlayer 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 VATlayer 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 VATlayer 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 VATlayer
VATlayer provides a reliable and easy-to-use API for VAT number validation, VAT rates, and VAT compliance. It is designed to help businesses manage their VAT obligations efficiently and accurately.
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
