Hi @Arun Sharmaโ, Database objects naming conventions and coding standards are crucial to maintaining consistency, readability, and manageability in a data engineering project.
In Databricks, you can use the naming conventions and coding norms for the Bronze, Silver, and Gold layers.
- General Naming Conventions:
- Use lowercase letters for all object names (tables, views, columns, etc.).
- Separate words with underscores for readability.
- Be descriptive and concise. Use names that indicate the purpose of the object.
- Avoid using reserved keywords or special characters.
- Bronze Layer (Raw Data Layer):
- Table Naming Convention: Use the prefix "bronze_" followed by the source system or data source and the object's nameโfor example, bronze_salesforce_opportunities.
- File Format: Store data in Delta Lake format to leverage its performance, ACID transactions, and schema evolution capabilities.
- Partitioning: Use partition columns that best suit your data access patterns, such as date or timestamp.
- Silver Layer (Cleansed and Enriched Data Layer):
- Table Naming Convention: Use the prefix "silver_" followed by the functional area or business domain and the object's nameโfor example, silver_finance_transactions.
- File Format: Use Delta Lake format for storing the data.
- Partitioning: Choose appropriate partition columns, considering data access patterns and performance implications.
- Data Cleansing and Enrichment: Apply necessary data quality checks, type conversions, and enrichment processes.
- Documentation: Document the transformation logic and any assumptions made during the cleansing and enrichment process.
- Gold Layer (Aggregated and Business Ready Data Layer):
- Table Naming Convention: Use the prefix "gold_" followed by the functional area or business domain and the object's name. For example, gold_sales_monthly_summary.
- File Format: Store the data in Delta Lake format.
- Partitioning: Choose appropriate partition columns, considering data access patterns and performance implications.
- Aggregations: Perform aggregations and calculations as required by the business requirements.
- Documentation: Document the aggregation logic and any assumptions made during the process.
- Code Organization:
- Separate your code into different notebooks for each layer (Bronze, Silver, Gold) and maintain a clear hierarchy for ease of maintenance.
- Follow best code formatting and readability practices, such as user comments, consistent indentation, and modularization.
- Use version control systems like Git to manage your codebase and track changes.
Following these naming conventions and coding standards allows you to maintain a well-structured, easily understandable, and maintainable data engineering project in Databricks.
