Connect Disqus and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Comment
New Flagged Comment
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Disqus and TimescaleDB
Enable Integrations or automations with these events of Disqus and TimescaleDB
Triggers
New Comment
Triggers when a new comment is left on a Disqus-enabled forum.
New Flagged Comment
Triggers when a Disqus comment is flagged.
Actions
Create Forum
Creates a new forum.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Disqus and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Disqus triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Disqus 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 Disqus 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 Disqus 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 Disqus
Disqus is a global comment system that improves discussion on websites and connects conversations across the web. It provides a platform for users to engage in discussions, share opinions, and interact with content creators and other users.
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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