IntegrationsTypebotTimescaleDB
Typebot + TimescaleDB

Connect Typebot and TimescaleDB to Build Intelligent Automations

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Typebot

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Typebot and TimescaleDB?

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

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

Absolutely. You can customize how Typebot 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 Typebot and TimescaleDB?

The data sync between Typebot 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 Typebot 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 Typebot and TimescaleDB?

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

Typebot

About Typebot

Typebot is a powerful platform designed to create and manage interactive chatbots for various applications. It allows users to build custom chatbots without any coding knowledge, making it accessible for businesses of all sizes to enhance customer engagement and automate communication.

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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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