Kimball methodology, also known as the Kimball Lifecycle, is an approach to data warehousing and business intelligence (BI) developed by Ralph Kimball. It provides a practical and iterative framework for designing, developing, and maintaining data warehouse systems. The methodology emphasizes simplicity, flexibility, and a focus on delivering business value.

Here are the key components and principles of the Kimball methodology:

1. Dimensional Modeling: At the core of the Kimball methodology is dimensional modeling, which involves designing the data warehouse around business processes and analytical requirements. Dimensional models are built using star schemas or snowflake schemas, which consist of fact tables (containing numeric measures) surrounded by dimension tables (containing descriptive attributes). This approach enables easy querying and analysis of data.
2. Bottom-Up Approach: The Kimball methodology follows a bottom-up approach, where the data warehouse is built incrementally starting with small, specific projects called data marts. Data marts are focused subsets of the data warehouse that address specific business areas or departments. They are designed to deliver business value quickly and can later be integrated into a larger enterprise data warehouse.
3. Business Dimensional Lifecycle: The methodology defines a four-step process called the Business Dimensional Lifecycle, which guides the development of data warehouse systems. The steps include requirements gathering, dimensional modeling, physical design, and implementation. This iterative process ensures that the data warehouse evolves to meet changing business needs.
4. Conformed Dimensions and Facts: Conformed dimensions and facts are shared across multiple data marts to ensure consistency and integration. Conformed dimensions represent common business entities, such as customers or products, and are designed to have consistent attributes and hierarchies. Conformed facts represent the measures that are consistently defined across different areas of the data warehouse.
5. ETL (Extract, Transform, Load): The Kimball methodology emphasizes the importance of effective ETL processes to populate the data warehouse. ETL processes extract data from source systems, transform it to fit the dimensional model, and load it into the data warehouse. The methodology advocates for using a staging area to cleanse and integrate data before loading it into the warehouse.
6. Iterative Development and Agile Principles: The Kimball methodology encourages an iterative and agile approach to data warehousing. It promotes delivering incremental value to business users through frequent releases and continuous feedback. This enables the data warehouse to adapt and evolve over time as new requirements emerge.
7. User-Focused Delivery: The methodology emphasizes the importance of delivering data and BI solutions that meet the needs of business users. It encourages close collaboration between business stakeholders, data modelers, and developers to ensure that the data warehouse provides actionable insights and supports decision-making.

Overall, the Kimball methodology provides a practical and business-centric approach to data warehousing and BI. It focuses on delivering value quickly, maintaining simplicity, and enabling iterative development to meet changing business requirements.