Connect JobNimbus and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
Task Created Modified or Deleted
New Estimate
Invoice Sent
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of JobNimbus and TimescaleDB
Enable Integrations or automations with these events of JobNimbus and TimescaleDB
Triggers
Task Created Modified or Deleted
Trigger when a new task is created, modified or deleted.
New Estimate
Trigger when New Estimate is created.
Invoice Sent
Trigger when a invoice status is changed to sent.
Actions
Create Contact
create a new contact within JobNimbus.
List All Contacts
Retrieves the details of all contacts
Create Job
Creates a new job, requiring a Contact ID from Create Contact.
Find Contact by ID
Find Contact by ID
List All Jobs
Retrieves the details of all the jobs within a JobNimbus account.
List All Tasks
Retrieves the details of all the tasks within a JobNimbus account.
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Frequently Asked Questions
To start, connect both your JobNimbus and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in JobNimbus triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how JobNimbus 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 JobNimbus 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 JobNimbus 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 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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