IntegrationsMeeting BaaSTimescaleDB
Meeting BaaS + TimescaleDB

Connect Meeting BaaS and TimescaleDB to Build Intelligent Automations

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Meeting BaaS

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Meeting BaaS and TimescaleDB?

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

Can we customize how data from Meeting BaaS is recorded in TimescaleDB?

Absolutely. You can customize how Meeting BaaS 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 Meeting BaaS and TimescaleDB?

The data sync between Meeting BaaS 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 Meeting BaaS 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 Meeting BaaS and TimescaleDB?

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

Meeting BaaS

About Meeting BaaS

Meeting BaaS is a comprehensive platform designed to streamline and enhance the process of organizing, scheduling, and conducting meetings. It offers a suite of tools to facilitate seamless communication, collaboration, and documentation, making it an ideal solution for businesses and teams looking to improve their meeting efficiency and productivity.

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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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