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            Replicate Metadata Across Data Spaces Using Local Data Kit Deployment

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            Replicate Metadata Across Data Spaces Using Local Data Kit Deployment

            Use a local data kit deployment to replicate Data Cloud metadata and process definitions across multiple data spaces within the same sandbox org. Packaging configurations into a local data kit reduces manual effort, ensures consistency, and accelerates time to value when your data space strategy requires standardized definitions across different spaces. Standardization is especially important for deployments across geographies or brands where processes are defined centrally.

            Required Editions

            Available in: All Editions supported by Data 360. See Data 360 edition availability.
            Available with add-on license: Data Spaces
            Permission Sets Needed
            To access Data Cloud Setup

            System admin profile OR

            Data Cloud Architect permission set

            Previously, replicating configurations such as calculated insights and segments required manual re-creation in each data space, which was time-consuming and prone to error. With local data kit deployment, Data 360 performs a pure upsert, automatically updating existing components or creating new ones with the appropriate data space prefix.

            • Verify that you're working in a Data 360 sandbox environment.
            • Log in to the default data space.
            • Make sure that your metadata and process definitions are finalized in the default data space.
            Note
            Note Local data kit deployment supports standard data kits only. To move changes from sandbox to production, use a DevOps data kit.

            These high-level steps replicate metadata and promote it to production:

            1. Create your data model objects (DMO) mappings, calculated insights, and segments in the default data space of your sandbox.
            2. Package your finalized configurations into a standard local data kit.
            3. Use the one-click deployment for each target data space within the sandbox.
            4. Add the necessary filters to the data lake objects (DLO) mapped into each of the target data spaces.
            5. Create a DevOps data kit for each data space to deploy the configurations to your production environment.

            See Also

             
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