Before you can run a data deployment, you must manually enable Gearset's data loader. Running a data deployment in Gearset is a quick and simple process:

Select source and target

  • Select the source and target orgs to be used and click CONFIGURE DEPLOYMENT

  • You can also deploy data to and from Salesforce DX scratch orgs. To find out how to configure and create scratch orgs within Gearset, see our support guide. However you will not be able to select a delegated org for deploying data.

Select primary objects to deploy

Gearset will now perform a Salesforce listMetadata call to retrieve the object list from the selected source and target organizations, and display the results in a tree view.

  • For each parent object in the tree you can specify the number of records to retrieve, up to a maximum of 100,000.

  • When a parent object is expanded, which you can do by clicking on it, Gearset checks for and lists any dependencies that are detected that can then be included within your data subset. 

  • Gearset will then traverse the dependency graph to determine any further indirect dependencies necessary to deploy records from the selected objects.

  • Use the object filter settings to customise your data set and retrieve and deploy records based on field value.

  • Once the objects have been selected, click NEXT.

Select matching configuration and related objects

  • Choose how you want to match existing records, which related objects (this is determined by lookup and master-detail fields, see this article for more info) to include, and customize their field mapping for upserting.

  • The field chosen to match records for upserting can be an External ID field or a ID Lookup field. (You can create a custom field with the External ID property, and view this in Salesforce. The ID Lookup property is visible only via the API in Workbench.) If neither of those types of field exist, new records will created.

  • You can upsert with ID when any of these are true:

    • source is backup and target is the original org

    • source is the same org as the target

    • source is a sandbox, target is a sandbox, they're sandboxes of the same org

    • source is a prod org, target is a sandbox, target is a sandbox of the source org

  • Choose whether you'd like the data deployment to continue if it hits an error deploying a record. In that case, if a deployment step fails, the remaining deployment steps will continue to execute, ignoring the errors encountered and deploying as many of the records as possible. Or, if you want the deployment to stop in the event of an execution step error, select the Stop deploying remaining records option.

  • Click NEXT to continue and configure data masking.

Select data masking options

  • Choose whether you want to mask any fields to obfuscate their values. If a field is masked, Gearset will deploy fake values instead of the real values for that field, concealing sensitive customer information. Learn more about data masking.

  • Once you've chosen which fields to mask, click PRE-DEPLOYMENT SUMMARY to review the data to be deployed. Gearset will create and list out the steps we’re about to take in your data deployment.

Deploy the data records

  • You'll be able to see the object records to be deployed, the actions Gearset will take to retrieve the records, such as fetching, including, creating and upserting, as well as the field count and any applied filters.

  • Click on DEPLOY DATA to deploy the selected data to the target org. When the deployment is running, each stage will mark as completed and show you how many records have been processed in each step.

  • If you're deploying to a production org, you'll be asked to confirm that you're happy to proceed before the deployment runs.

  • Once complete, you'll have your subset of data available for any testing and debugging you'd like to perform on the target org.

  • The deployment will be stored in your data deployment history in the app. From here, you can view previous data deployments, their status, and view the details of which records were moved. Learn more about data deployment history here.

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