Connect Numverify and TimescaleDB to Build Intelligent Automations

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Frequently Asked Questions

How do I start an integration between Numverify and TimescaleDB?

To start, connect both your Numverify and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Numverify triggers actions in TimescaleDB (or vice versa).

Can we customize how data from Numverify is recorded in TimescaleDB?

Absolutely. You can customize how Numverify 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.

How often does the data sync between Numverify and TimescaleDB?

The data sync between Numverify and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.

Can I filter or transform data before sending it from Numverify to TimescaleDB?

Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.

Is it possible to add conditions to the integration between Numverify and TimescaleDB?

Yes, you can set conditional logic to control the flow of data between Numverify 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.

Numverify

About Numverify

Numverify is a powerful API service that provides global phone number validation and lookup. It offers real-time validation, ensuring that phone numbers are accurate and up-to-date. This service is essential for businesses that require reliable phone number verification for customer contact information, fraud prevention, and data accuracy.

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TimescaleDB

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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