IntegrationsKeySenderTimescaleDB
KeySender + TimescaleDB

Connect KeySender and TimescaleDB to Build Intelligent Automations

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KeySender

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between KeySender and TimescaleDB?

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

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

Absolutely. You can customize how KeySender 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 KeySender and TimescaleDB?

The data sync between KeySender 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 KeySender 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 KeySender and TimescaleDB?

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

KeySender

About KeySender

KeySender is a powerful tool designed to automate the process of sending and managing digital keys and codes. It streamlines the distribution of digital products, ensuring secure and efficient delivery to customers. Ideal for businesses dealing with software licenses, gift cards, and other digital goods, KeySender simplifies the management of digital assets and enhances customer satisfaction.

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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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