Tutorial: Working with Data Environments

Understanding data environments and how to use them to isolate and protect your data.

What is a Data Environment?​

A data environment is a physically independent space where all your data, settings, workflows, and scripts reside. Environments are typically used to separate:

• Production — live, trusted data
• Test or QA — data used for testing features without affecting production
• Develop — experimental or in-progress work

Everything in an environment is isolated from other environments, including:

Actions, Audit records, Calendars, Curve management, Data, Extractors, Groups, Objects, Processes, Report configurations, Security policies, Scripts, Transformers, Types, and Workflows.

Creating an Environment​

//#region
training = Object()
training.description = "Training environment"
save ${environment:training}
print "Training environment created"
//#endregion

Switching to an Environment​

Use the use command to direct all subsequent reads and writes to a named environment:

use training

Any entity saved after this command will be stored in the training environment, not production.

Listing Environments​

//#region
environments = find ${environment}
print environments
print "There are currently " + environments.size + " environments"
//#endregion

Each environment object contains:

Deleting an Environment​

//#region
delete ${environment:"training"}
print "Environment deleted"
//#endregion

Best Practice​

Always do the following before running any training or experimental code:

1. Create a dedicated environment (e.g. training)
2. Add use training at the top of every script
3. Verify your changes in the training environment before applying anything to production