Specifically, we will leverage data warehouse ideas, SQL methodologies, and BI-focused schema architecture to design and implement a Data Mart in this article.

Step 1. Understand Business Requirements for the Data Warehouse

  • Engage business stakeholders to define reporting needs.
  • Identify key facts (e.g., total sales, loan balance) and dimensions (e.g., product, branch, customer).
  • Define the grain of the data mart (e.g., "daily branch sales").

Step 2. Design the Data Warehouse Schema (Dimensional Modeling)
Use a Star Schema or Snowflake Schema, optimized for SQL queries.

  • Fact Table (e.g., Fact_Sales): Contains numeric metrics and foreign keys
  • Dimension Tables (e.g., Dim_Customer, Dim_Product): Contain descriptive attributes to slice and filter data

Step 3. Source to Target Mapping (STM)

  • Identify source systems (e.g., OLTP DBs like Core Banking, ERP).
  • Document field-level mappings from source to warehouse tables.
  • Define SQL-based transformations (e.g., date format conversion, category lookup).

Step 4. Build the ETL in SQL or ELT with dbt
Using SQL or dbt (Data Build Tool).

  • Extract: Pull raw data from staging tables.
  • Transform: Use SQL CTEs or dbt models to clean and join.
  • Load: Insert transformed data into fact/dimension tables.

Tips

  • Use SCD Type 2 for dimensions
  • Maintain surrogate keys
  • Use indexes and partitions for performance

Step 5. Connect to BI Tools

  • Expose the SQL-based data mart to Power BI, Tableau, or Looker.
  • Use pre-aggregated views for performance.
  • Provide semantic consistency by using dimensional hierarchies.

Step 6. Testing and Optimization

  • Write SQL test cases for row counts, null checks, and data accuracy.
  • Schedule SQL scripts or dbt jobs for refresh.
  • Monitor performance with query logs or warehouse query history.

Conclusion
Building a Data Mart using SQL and data warehousing best practices is essential for scalable and high-performing BI. With the right design and automation in tools like dbt or SQL scripts, your organization can gain accurate, fast insights. In the next article, we’ll walk through building a Sales Data Mart in SQL + dbt with dimensional modeling and snapshotting logic.

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