Considerations for Data Kits in Data 360

Refer to these considerations when using a data kit to package Data 360 features.

• Create your standard data kit from Developer Edition orgs only.
• Data Connector Framework (DCF) Data Streams: The Data Connector Framework (DCF) controls how you set up and manage connectors in Data 360. When you deploy DCF-based data streams in a standard data kit, you can either use a packaged connector or select a preconfigured connector in the target org during deployment.
• Non-DCF Data Streams: When you deploy the non-DCF data stream, select a preconfigured connector in the target org. The connector details aren't included during the deployment process.
• DevOps Data Kit: When you deploy data streams using a DevOps data kit, Data 360 automatically adds the connector information to the target org.
• Data Model Objects (DMOs): When you deploy standard and custom Data Model Objects (DMOs) from a sandbox, the associated relationships and tags are automatically added during the deployment process. However, tags that are explicitly assigned to other DMO types, such as derived DMOs or curated DMOs, aren't included in the deployment.
• When you add a data model object to a standard data kit, custom data models are added as is. If you select a standard data model, only the custom mappings and custom fields added on top of the data model are packaged.
• Zero Copy Data Stream: For Zero Copy data streams using Google BigQuery, Snowflake, Databricks, or Redshift connections, make sure that the database, schema, and table names are consistent between your publisher and target orgs.
• Data Lake Objects (DLOs): When you add DLOs to a data kit using the Add option under the Data Kits tab, only standalone DLOs are displayed on the page.
• Unstructured Data Lake Objects (UDLOs): When you add a UDLO to a data kit, the mapped unstructured data model object (UDMO) is automatically included in the data kit.
• Calculated Insights: When you add an insight to a data kit, manually add the insight's dependencies. Dependencies can include child-calculated insights, data model objects, data lake object, and data graphs. Without the dependencies, the deployment of the insight can fail.
• Activations: When building a data kit, add and save your activations in small batches. Attempting to save a large number of activations simultaneously can cause the operation to time out and fail.