Connect Streamtime and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Streamtime
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Streamtime and TimescaleDB
Enable Integrations or automations with these events of Streamtime and TimescaleDB
Actions
Create Personal ToDo
Creates a new Personal To-Do record in the system.
Create Job Comment
Adds a comment to a job.
Create Job
Creates a new Job in the system.
Create Label
Add a label to a Job,Quote,Invoice,Logged Expense,Company,Contact and User.
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Frequently Asked Questions
To start, connect both your Streamtime and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Streamtime triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Streamtime 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 Streamtime 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 Streamtime 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 Streamtime
Streamtime is a project management and time tracking tool designed for creative teams. It helps streamline workflows, manage tasks, and track time efficiently, making it ideal for agencies and creative professionals.
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.
Learn More
