Data 360 Data Kits package metadata and process definitions, but they do not make every component portable by themselves. A deployment can fail—or stop partway through—because the kit type, transport method, dependencies, names, connections, data space, or publishing sequence does not fit the target environment. The key is to treat a kit as a migration mechanism with prerequisites, not as a guarantee of identical behavior across orgs.
What a Data Kit does—and what it doesn’t
A Data Kit packages Data 360 components for deployment. It does not automatically supply every external dependency, reconcile differences between source and target environments, or make all deployment paths interchangeable. Salesforce distinguishes two kit types: Standard Data Kits for packaging and sharing solutions, and DevOps Data Kits for migrating metadata between environments such as sandbox and production.
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Standard Data Kits
Salesforce’s guidance is to create a Standard Data Kit from the default data space and deploy it to a data space in the target org. This type is intended to package and share Data 360 solutions. Its available migration methods depend on the source and target environment pair.
DevOps Data Kits
A DevOps Data Kit is intended for metadata migration between environments. It is created from a data space and deployed to the corresponding data space in the target org. The target data space may need to exist first.
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Do not choose a kit type solely by what is easiest to publish. Salesforce says objects deployed through a Standard or DevOps Data Kit can only be updated by modifying and redeploying that same kit type. The two types are not interchangeable for updates, and manually created objects cannot be updated through a Data Kit. Salesforce: Data 360 Data Kit considerations and common issues
Which deployment method fits your environments?
There is no universal Data Kit transport. Salesforce’s migration guidance, updated July 9, 2026, lists these high-level paths:
| Source and target | Standard Data Kit | DevOps Data Kit |
|---|---|---|
| Production ↔ Production | Package Manager, from the default data space | Salesforce CLI |
| Production ↔ Sandbox | Package Manager, from the default data space | Change Sets or Salesforce CLI |
| Sandbox ↔ Sandbox | Package Manager, from the default data space | Change Sets or Salesforce CLI |
In Salesforce’s matrix, Change Sets are limited to sandboxes created from the same production environment. The same conditions apply in either direction for production/sandbox migrations. Check the current Data Kit migration guidance before publishing: these are supported paths, not a guarantee that every component is portable.
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Why a deployment can fail or behave differently
The kit type or transport does not match the job
Standard and DevOps Data Kits serve different purposes and have different supported migration methods. Salesforce identifies a mismatch between kit type and deployment method as a common issue. First confirm that the kit type suits the goal, then confirm that the selected transport is supported for the exact source and target combination.
Required dependencies are not in the kit
Do not assume a referenced object brings every dependency along. Salesforce says that when a Data Model Object (DMO) or its fields are dependencies, add the DMO and relevant fields explicitly. A Calculated Insight may also depend on child insights, DMOs, Data Lake Objects (DLOs), and data graphs; include the necessary components in the kit.
For DLO-to-DMO output mappings, include the output DLO itself. DLOs created from a Data Stream and DLOs created from a Data Transform are not interchangeable for kit inclusion. A DLO linked to a Data Stream is automatically included with that stream and cannot be manually added; only certain transform-created DLOs can be added. See Salesforce’s component considerations and common issues.
Names or connections differ between orgs
In the documented packaged-component deployment flow, corresponding external project, database, dataset, schema, and table names must match between source and target. The kit captures source connection names rather than remapping them during deployment, so a mismatch can cause failure.
Connector handling also depends on the stream and kit type. For Standard Data Kits, a non-DCF stream requires a connector already configured in the target org; connector details are not included in deployment. DevOps Data Kits add connector information to the target org. Streams are associated with connections, so include the required connection when deploying stream changes. Salesforce describes these distinctions in its Data Kit considerations.
The data space or component has a restriction
Standard Data Kits are created from the default data space. DevOps Data Kits can be created from any data space, but must be deployed to the corresponding data space in the target org. Check that the target space exists and, where relevant, that its data-space prefix matches. Salesforce also says that Data Transforms in a non-default data space cannot currently be deployed through Data Kits.
An earlier component failed, so later components were skipped
Deployment follows the publisher-defined sequence. If a component fails, subsequent components in the sequence are not deployed. That means a partial result can look like several independent failures when later components were simply never reached. For DevOps Change Set workflows, inspect the publishing sequence and keep it current: Salesforce says a manually edited sequence is not automatically updated when kit components change. Salesforce: Deploy Data Kit components
Activations or schedules changed operationally
Salesforce advises adding and saving activations in small batches because saving many at once can time out. Also review batch Data Transform schedules: the schedule is included in the kit and is active in the destination after installation. Validate whether that activation is appropriate before treating the deployment as complete.
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- Choose the kit for the goal. Use Standard for packaging and sharing a solution; use DevOps for environment migration.
- Match the transport to the environment pair. Check Salesforce’s migration matrix for the exact source and target, including the same-production-origin constraint on sandbox Change Sets.
- Confirm the target data space. Ensure it exists and corresponds to the source space for a DevOps deployment. Confirm any relevant data-space prefix requirements.
- Make dependencies explicit. Add required DMOs and fields, Calculated Insight dependencies, output DLOs, and other needed components.
- Compare names and connection setup. Check external project, database, dataset, schema, table, and connection names where the component type requires them. Verify connector configuration for non-DCF streams in a Standard kit, and include connections needed by stream changes.
- Review component scope and order. Confirm that the intended DLO types can be included, then inspect the publisher-defined sequence so prerequisites precede dependent components.
- Plan for operational effects. Save activations in small batches and review batch transform schedules that will be active in the target.
- Deploy and verify the outcome. Inspect Deployment History, identify the first failure, and check whether later components were skipped. Verify downstream components rather than assuming the whole kit deployed.
- Test before production. Where possible, validate the deployment and any resulting schedule behavior in an appropriate sandbox before publishing to production.
This checklist reflects Salesforce’s published deployment guidance; it is not a claim of independent deployment testing. For unresolved migration issues, Salesforce directs users to component-specific considerations and its Support Center. Migration guidance
What to check after a failed deployment
- Open Deployment History and identify the earliest failed component, rather than starting with a later component that may have been skipped.
- Compare the failing component with the target org: confirm its data space, dependencies, external names, connector setup, and required connection.
- Check the kit type and transport against the source/target pair. If changing the kit type, account for Salesforce’s rule that objects deployed through one type cannot be updated using the other.
- Correct the component or sequence, then redeploy using the appropriate kit and supported mechanism. Verify each downstream component and review active schedules in the destination.
Why predictability has limits
Data Kits make Data 360 configuration deployable, but the deployment still depends on environment-specific state and component rules. Salesforce documents categorical migration options and known constraints, not a deployment success rate or a quantified predictability measure. For context, Salesforce says Data Cloud was rebranded as Data 360 on October 14, 2025, with functionality and content unchanged during the transition; some documentation may therefore retain the older name. Salesforce: Data Cloud is now Data 360
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