Connect Mokaform and TimescaleDB to Build Intelligent Automations

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Mokaform

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TimescaleDB

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Enable Integrations or automations with these events of Mokaform and TimescaleDB

Enable Integrations or automations with these events of Mokaform and TimescaleDB

Triggers

New Form Submission

New Form Submission

Runs when new form submission is submitted

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Frequently Asked Questions

How do I start an integration between Mokaform and TimescaleDB?

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

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

Absolutely. You can customize how Mokaform 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 Mokaform and TimescaleDB?

The data sync between Mokaform 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 Mokaform 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 Mokaform and TimescaleDB?

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

Mokaform

About Mokaform

Mokaform is a versatile platform designed for creating and managing online forms and surveys, enabling users to collect data efficiently and effectively.

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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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