Code reusability for silver table transformations

[jitesh]

How/how many databricks notebooks should be created to populate multiple silver delta tables, all having different and complex transformations ? What's the best practice -

1. create a notebook each for a silver table ?

2. push SQL transformation logic to a config table ? And create a single reusble notebook for to be used by all the tables ? Or some other approach ?

PS - Created just a single notebook to convert the raw parquet tables to delta format, as the tranfromations were generic for all the bronze tables.

[Kaniz]

Hi @jitesh, When organizing your Databricks Notebooks for multiple silver Delta tables with different and complex transformations, it’s essential to follow best practices.

Here are some recommendations:

1. Separate Notebooks for Each Layer:

   • Bronze Layer (Raw Data Layer): Create a separate notebook for each bronze table. Use the prefix “bronze_” followed by the source system or data source and the object’s name (e.g., bronze_salesforce_opportunities). Store data in Delta Lake format for performance, ACID transactions, and schema evolution capabilities.
   • Silver Layer (Cleansed and Enriched Data Layer): Similarly, create separate notebooks for each silver table. Use the prefix “silver_” followed by the functional area or business domain (e.g., silver_finance_transactions). Apply necessary data quality checks, type conversions, and enrichment processes.
   • Gold Layer (Aggregated and Business-Ready Data Layer): Again, create separate notebooks for each gold table. Use the prefix “gold_” followed by the functional area or business domain (e.g., gold_sales_monthly_summary). Perform aggregations and calculations as required by business requirements.

2. Code Organization and Hierarchy:

   • Maintain a clear hierarchy by organizing your code into different notebooks for each layer (Bronze, Silver, Gold). This separation makes it easier to manage and maintain.
   • Follow best code formatting and readability practices, such as adding user comments, consistent indentation, and modularization.
   • Use version control systems like Git to manage your codebase and track changes effectively¹.

3. Additional Considerations:

   • Prioritize denormalization for key joins in the Gold layer. Strategically denormalize specific dimensions frequently joined with fact tables to balance flexibility with query performance.
   • Explore materialized views in the Gold layer to pre-join frequently accessed data, potentially mitig...².

Remember that the right approach may vary based on your specific use case and requirements. However, following these guidelines will help you maintain consistency, readability, and manageability in your data engineering project. 😊🚀