IntegrationsRecraftTimescaleDB
Recraft + TimescaleDB

Connect Recraft and TimescaleDB to Build Intelligent Automations

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Recraft

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Recraft and TimescaleDB?

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

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

Absolutely. You can customize how Recraft 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 Recraft and TimescaleDB?

The data sync between Recraft 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 Recraft 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 Recraft and TimescaleDB?

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

Recraft

About Recraft

Recraft is an innovative AI-powered platform designed to enhance creativity and productivity. It offers a suite of tools that leverage artificial intelligence to assist users in generating content, designing visuals, and automating repetitive tasks. With Recraft, users can streamline their creative processes and focus on what truly matters, making it an essential tool for professionals and creatives alike.

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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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