IntegrationsCalamariTimescaleDB
Calamari + TimescaleDB

Connect Calamari and TimescaleDB to Build Intelligent Automations

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Calamari

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Calamari and TimescaleDB?

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

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

Absolutely. You can customize how Calamari 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 Calamari and TimescaleDB?

The data sync between Calamari 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 Calamari 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 Calamari and TimescaleDB?

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

Calamari

About Calamari

Calamari is a comprehensive HR management tool designed to streamline attendance tracking, leave management, and employee scheduling. It offers features such as clock-in/out, leave requests, and team calendar integration, making it ideal for businesses looking to enhance their human resources operations.

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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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