In this guide, we'll focus specifically on designing and implementing a Data Mart using data warehouse principles, SQL techniques, and BI-focused schema design.
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.