Connect Runn and TimescaleDB to Build Intelligent Automations
Enable Integrations or automations with these events of Runn and TimescaleDB
Enable Integrations or automations with these events of Runn and TimescaleDB
Actions
Create project
Create a new Runn project for the chosen client, with optional budget, team, and rate settings.
Create client
Create a new client in Runn using a name and optional website.
Create Person
Add a person to Runn so they can be assigned a role, team, and schedule.
Create role
Create a new role in Runn to assign to people and projects.
Create skill
Add a new skill to Runn so you can assign and track expertise.
Assign person to project
Add a person to a project for selected dates with daily time, role, and billable status.
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Frequently Asked Questions
To start, connect both your Runn and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Runn triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Runn 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 Runn 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 Runn 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 Runn
Runn is a powerful tool designed to streamline project management and resource planning. It offers intuitive features for scheduling, tracking, and forecasting, making it ideal for teams looking to optimize their workflow and resource allocation.
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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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