Connect Privyr and TimescaleDB to Build Intelligent Automations

Choose a Trigger

Privyr

When this happens...

Choose an Action

TimescaleDB

Automatically do this!

Enable Integrations or automations with these events of Privyr and TimescaleDB

Enable Integrations or automations with these events of Privyr and TimescaleDB

Actions

Create Client

Create Client

Creates a new client

Request a new Action for Privyr

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Frequently Asked Questions

How do I start an integration between Privyr and TimescaleDB?

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

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

Absolutely. You can customize how Privyr 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 Privyr and TimescaleDB?

The data sync between Privyr 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 Privyr 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 Privyr and TimescaleDB?

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

Privyr

About Privyr

Privyr is a mobile CRM built for sales professionals who run their businesses from their phone. It works with WhatsApp, iMessage, SMS, email, and other popular chat apps.

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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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