IntegrationsNyckelTimescaleDB
Nyckel + TimescaleDB

Connect Nyckel and TimescaleDB to Build Intelligent Automations

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Nyckel and TimescaleDB?

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

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

Absolutely. You can customize how Nyckel 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 Nyckel and TimescaleDB?

The data sync between Nyckel 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 Nyckel 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 Nyckel and TimescaleDB?

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

Nyckel

About Nyckel

Nyckel is a platform that provides machine learning as a service, enabling users to easily integrate AI capabilities into their applications without needing extensive knowledge in data science or machine learning. It offers tools for creating, training, and deploying machine learning models with ease.

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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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