Hi @pmscorca ,
Thanks for using Fabric Community.
While there's no official standard or document similar to ADF or Synapse Analytics,
However, there are some general best practices and recommendations to follow for consistency and clarity:
- Descriptive Names: Use clear and concise names that reflect the purpose or content of the item. For example, "CustomerSalesLakehouse" or "MarketingCampaignAnalysisNotebook".
- Consistency: Maintain a consistent naming style throughout your Fabric environment. Decide on a case format (uppercase, lowercase, camelCase) and stick to it.
- Versioning: If applicable, consider including version information in the name for easy tracking of changes. (e.g., "CustomerSalesPipeline_v2")
- Avoid Special Characters: Stick to alphanumeric characters, underscores (_), and hyphens (-) for better readability and compatibility across tools.
Here are some additional suggestions:
- Lakehouses: Use descriptive names reflecting the data domain or purpose (e.g., "FinancialDataLakehouse").
- Data Warehouses: Similar to lakehouses, use names that indicate the data warehouse's function (e.g., "CustomerAnalyticsWarehouse").
- Notebooks: Include details on the notebook's content or analysis performed (e.g., "DataCleaning_Notebook" or "MachineLearning_Model_Training").
- Dataflow Gen2: If applicable, incorporate the dataflow's purpose or processed data in the name (e.g., "SalesDataTransformation_Dataflow").
Remember, consistency is key. Establish a naming convention within your team or organization and ensure everyone follows it for better organization and understandability within your Fabric environment.
Similar Thread: Naming Conventions for Microsoft Fabric objects/ar... - Microsoft Fabric Community
Hope this is helpful. Please do let me know incase of further queries.
