Implementation of a Data Warehouse with Data Marts Using DBT and SQL Server

Introduction

Implementing a data warehouse with data marts is a crucial step in enhancing data-driven decision-making. This blog post outlines the structured approach taken to develop a data warehouse using SQL Server and Data Build Tool (DBT), while leveraging GitHub for version control. The process encompasses high-level and detailed dimensional modeling, database setup, DBT configuration, implementation, and documentation hosting via GitHub Pages.

For a detailed report, visit: Implementation Report.

Project Workflow

The implementation follows a structured approach with key phases:

High-Level Dimensional Model

The first step involves designing a high-level dimensional model that accurately represents business data requirements. This phase includes:

• Identifying key business processes.
• Defining fact and dimension tables.
• Establishing relationships between entities.
• Choosing between a star or snowflake schema based on business needs.

Detailed Dimensional Model

Building on the high-level model, a detailed dimensional model is designed to ensure effective data structuring. This involves:

• Defining attributes and keys for each table.
• Specifying granularity levels for fact tables.
• Ensuring the right balance between normalization and denormalization for performance optimization.

Database Setup in SQL Server

A structured database is set up in SQL Server to house the data warehouse and data marts. This step includes:

• Configuring the SQL Server instance.
• Creating schemas for different data marts.
• Implementing tables as per the dimensional model.
• Establishing indexes and constraints for query optimization.

Setting Up DBT Development Environment

DBT is utilized to manage transformations, ensure data integrity, and optimize query performance. The setup includes:

• Installing DBT and configuring it for SQL Server.
• Defining a DBT project structure.
• Establishing a connection between DBT and SQL Server.
• Integrating GitHub for version control and collaboration.

Implementation of Dimensional Model with DBT

The dimensional model is implemented using DBT with a focus on efficiency and maintainability. This phase involves:

• Creating DBT models for fact and dimension tables.
• Writing SQL transformations to populate and manage data.
• Implementing tests and validations to ensure data accuracy.
• Utilizing DBT macros for reusable logic and efficiency.

Generating and Hosting DBT Documentation

To enhance transparency and maintainability, DBT documentation is generated and hosted as a static site using GitHub Pages. The steps include:

• Running dbt docs generate to create documentation.
• Using dbt docs serve to preview documentation locally.
• Committing documentation files to a GitHub repository.
• Configuring GitHub Pages to serve the documentation publicly.

Conclusion

The integration of SQL Server, DBT, and GitHub ensures a robust, scalable, and well-documented data warehouse with data marts. This structured approach enhances collaboration, maintains version control, and provides easy access to project documentation through GitHub Pages. As a result, businesses can leverage a streamlined data management and transformation process, ultimately supporting efficient reporting and analytics for better decision-making.

For an in-depth look at the implementation, check out the full report: Implementation Report.