Connect Confluence Cloud and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Page or BlogPost
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Confluence Cloud and TimescaleDB
Enable Integrations or automations with these events of Confluence Cloud and TimescaleDB
Triggers
New Page or BlogPost
Triggers when a new page or blog post is created.
Actions
List all Scopes
List all scopes
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Confluence Cloud and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Confluence Cloud triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Confluence Cloud 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 Confluence Cloud 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 Confluence Cloud 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 Confluence Cloud
Confluence is a collaboration tool developed by Atlassian that allows teams to manage projects, share documents, and enhance communication within teams. Confluence integrates seamlessly with Jira and supports a range of powerful plugins to extend its functionality.
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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