Connect SimpleKPI and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for SimpleKPI
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of SimpleKPI and TimescaleDB
Enable Integrations or automations with these events of SimpleKPI and TimescaleDB
Actions
Add KPI entry
Add a KPI data entry with a date, actual and target values, and optional notes.
Update KPI entry
Update an existing KPI entry for a given date with new actual, target, or notes.
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Frequently Asked Questions
To start, connect both your SimpleKPI and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in SimpleKPI triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how SimpleKPI 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 SimpleKPI 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 SimpleKPI 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 SimpleKPI
SimpleKPI is KPI Software for companies of all sizes who track Key Performance Indicators and metrics - complete with easy to use tools for visualizing data on dashboards, building engaging reports and analyzing performance.
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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