How to Build a Data Mart from Scratch: A Data Warehouse & SQL-Focused Guide for BI Teams

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.