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Salesforce Data 360 Data Kits package metadata and process definitions, but they do not make deployment environment-independent. A kit can fail or behave differently because of its type, the source and target orgs, missing dependencies, connection and naming differences, or deployment order. Before retrying, confirm you are using the right kit and transport for the migration, then check the target data space, component dependencies, and sequence.
What a Data Kit does—and what it does not
A Data Kit is a packaging and migration mechanism for Data 360 components. It is not a promise that every component will transfer unchanged between any two orgs. Salesforce distinguishes two kit types, with different purposes and deployment rules. Salesforce’s terminology changed from Data Cloud to Data 360 on October 14, 2025; its announcement said functionality and content remained unchanged during the transition, so some documentation may still use the older name. Salesforce: Data Cloud is now Data 360
Standard Data Kits: package and share solutions
Standard Data Kits are intended to package and share Data 360 solutions. Create one from the default data space, then deploy it to a data space in the target org. The documented migration matrix uses Package Manager for Standard kits across the listed production and sandbox pairings. Salesforce: Data Kit considerations and common issues · Salesforce: Data Kit migration options
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DevOps Data Kits: migrate metadata between environments
DevOps Data Kits are intended to migrate Data 360 metadata between environments, such as sandbox and production. Create one from a data space and deploy it to the corresponding data space in the target org. The target data space may need to exist before deployment. Unlike Standard kits, DevOps kits can be created from any data space, subject to component-specific restrictions. Salesforce: Data Kit considerations and common issues · Salesforce: Data Kit migration options
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Which kit and deployment method should you use?
Start with the purpose of the move, then check the source and target environment pair. Salesforce’s migration documentation, dated July 9, 2026, describes these high-level options. The same conditions apply in either direction for production/sandbox migrations. Use the current Salesforce matrix before publishing, because supported methods can change. This table describes supported paths, not a guarantee that every component is portable.
| Source and target | Standard Data Kit | DevOps Data Kit |
|---|---|---|
| Production ↔ Production | Package Manager; default data space | Salesforce CLI |
| Production ↔ Sandbox | Package Manager; default data space | Change Sets or Salesforce CLI |
| Sandbox ↔ Sandbox | Package Manager; default data space | Change Sets or Salesforce CLI; Change Sets are limited to sandboxes created from the same production environment |
See Salesforce’s Data Kit migration matrix for current method availability and component-specific considerations. Salesforce directs users to its Support Center for unresolved migration issues.
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Why did my Data Kit deployment fail or behave differently?
The kit type or transport does not match the job
A Standard kit used where a DevOps migration is intended—or the reverse—can lead to an unsupported path or update problem. The available transport depends on both kit type and source/target pairing; Package Manager, Change Sets, and Salesforce CLI are not interchangeable universal options. Check the migration matrix before creating or publishing the kit. Salesforce: Data Kit considerations and common issues · Salesforce: Data Kit migration options
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A dependency was not included
Do not assume a component’s dependencies will be inferred completely. If a Data Model Object (DMO) or its fields are dependencies, add the DMO and relevant fields explicitly. A Calculated Insight can require child insights, DMOs, Data Lake Objects (DLOs), and data graphs; include the required components in the kit. Salesforce: Data Kit considerations and common issues · Salesforce: Add Data Kit components
Names or connections differ between orgs
For the documented packaged-component deployment flow, corresponding project, database, dataset, schema, and table names must match. The kit captures source connection names and does not remap them at deployment, so a mismatch can prevent deployment. Check the target’s external names and connections rather than assuming the package will translate them. Salesforce: Deploy Data Kit components
Connector handling also depends on stream type and kit type. With Standard Data Kits, a non-Data Cloud Connector Framework (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 connection when deploying stream changes. Salesforce: Data Kit considerations and common issues
The requested component is outside the kit’s scope
DLO inclusion follows specific rules. A DLO linked to a Data Stream is included automatically with that stream and cannot be added manually. Only certain DLOs created by transforms can be added. A DLO created from a Data Stream is not interchangeable with one created from a Data Transform for kit inclusion. If a DLO-to-DMO output mapping is required, include the output DLO itself. Salesforce: Data Kit considerations and common issues · Salesforce: Add Data Kit components
The data space or metadata type is incompatible
Standard kits originate in the default data space. A DevOps kit targets the corresponding data space in the destination, which may need to be created first; check any required data-space prefixes as well as the data-space name. Salesforce’s common-issues guidance also says that Data Transforms from non-default data spaces cannot currently be deployed through Data Kits. Salesforce: Data Kit considerations and common issues · Salesforce: Data Kit migration options
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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. Salesforce states: “If a component fails during deployment, the process stops, and any subsequent components in the sequence aren’t deployed.” Inspect Deployment History and the sequence to find the first failure; do not infer that later components were installed just because they were in the kit. Salesforce: Deploy Data Kit components
For DevOps Change Set workflows, review the publishing sequence after changing the kit. Salesforce says it does not automatically update a manually edited sequence when kit components change, so keep that order in sync. Salesforce: Data Kit migration options
Activation or scheduling created an operational surprise
Salesforce advises adding and saving activations in small batches because saving many at once can time out. A batch Data Transform’s schedule is included and active in the destination after installation. Treat that as a live operational change: check the schedule and validate its expected behavior before deploying to production. Salesforce: Data Kit considerations and common issues
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Objects deployed with a Standard kit must be updated by modifying and redeploying that same Standard kit; DevOps and Standard kits cannot be used interchangeably to update them. The same-type rule applies to objects deployed with DevOps kits. Manually created objects cannot be updated through a Data Kit. Salesforce also says API-created DBT segments cannot be added by end users. Salesforce: Data Kit considerations and common issues
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- Choose the kit for the goal. Use Standard for packaging and sharing; use DevOps for environment-to-environment metadata migration.
- Confirm the supported transport. Match the kit type to the exact source and target environment pair in Salesforce’s migration matrix. For sandbox-to-sandbox Change Sets, confirm both sandboxes were created from the same production environment.
- Check the target data space. Confirm it exists where required, corresponds to the source space, and has the expected prefix. For Standard kits, verify the source is the default data space.
- Inventory dependencies and component scope. Add required DMO fields and Calculated Insight dependencies explicitly. Check DLO inclusion rules and include the output DLO when needed for a DLO-to-DMO mapping.
- Validate names, connectors, and connections. Where the component flow requires it, compare external project, database, dataset, schema, and table names. Check DCF versus non-DCF connector setup and include the connection for stream changes.
- Review the publishing sequence. Make sure prerequisites precede dependent components, and update any manually maintained sequence after changing kit contents.
- Publish and verify. Inspect Deployment History for the first failure and verify downstream components individually, since deployment stops after a component fails.
- Check operational effects. Confirm whether a batch transform schedule will be active in the destination. Test in an appropriate sandbox before production, and save activations in small batches.
Salesforce’s guidance documents these conditions and troubleshooting checks; it does not provide a deployment success rate or failure-rate statistic. Predictability is therefore best improved by validating the specific environment pair, kit contents, dependencies, and sequence—not by assuming a kit behaves identically everywhere.
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