Connect DeviantART and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for DeviantART
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of DeviantART and TimescaleDB
Enable Integrations or automations with these events of DeviantART and TimescaleDB
Actions
Get User Info
Get info of authenticated user
Create Folder
Create a new collection folder.
Update Folder
Update a folder.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Automate Instagram DM Replies, Reactions, and Team Alerts
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Frequently Asked Questions
To start, connect both your DeviantART and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in DeviantART triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how DeviantART 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.
The data sync between DeviantART and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between DeviantART 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.
About DeviantART
DeviantART is a vibrant online community showcasing various forms of user-made artwork. It allows artists to exhibit, promote, and share their creations with a global audience.
Learn MoreAbout 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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