Kimball Bottom-Up Data Warehouse Architecture

Dimensional Modeling:

This approach employs a star schema or snowflake schema to structure the data in each data mart.
Fact tables store quantitative data (e.g., sales revenue, quantity sold), while dimension tables store descriptive data (e.g., product details, time, customer information).

Data Marts First: Develops small, focused data marts for individual business functions, such as sales, marketing, and finance, addressing specific analytical needs.
Dimensional Modeling: Each data mart is structured using dimensional modeling (e.g., star schema), with fact tables for quantitative data and dimension tables for descriptive details, optimizing query efficiency.
Conformed Dimensions for Integration: Standardized dimensions like time, geography, and product enable seamless integration and consistent reporting across departments.
Enterprise Data Warehouse (EDW): As data marts are integrated, they form a central EDW, providing a unified source for cross-functional analytics and consolidated insights.
Business Intelligence (BI) and Analytics: The EDW supports various BI and analytics tools, allowing departments to leverage dashboards, reports, and insights to make informed decisions.

Incremental Implementation: Enables rapid deployment of data marts, providing immediate value without waiting for a fully developed data warehouse.
Flexibility and Adaptability: New data marts can be added and integrated as needed, allowing for scalable growth and adaptation to evolving business requirements.
Focused Optimization: Each data mart is optimized for specific business functions, ensuring efficient, targeted insights for departments.