Kimball, Inmon, and Data Vault are three popular methodologies or approaches used in data warehousing and business intelligence. Each approach has its own characteristics and focuses on different aspects of data modeling, integration, and architecture. Here's a brief overview of each methodology:
- Kimball Methodology:
The Kimball methodology, developed by Ralph Kimball, emphasizes dimensional modeling. It is known for its simplicity and practicality. The approach is based on the concept of a data warehouse as a collection of dimensional data marts. In this methodology, data is organized around business processes or subjects, and the focus is on creating a star schema or snowflake schema that represents the relationships between dimensions (descriptive attributes) and facts (measurable events). The Kimball methodology promotes the use of denormalized structures to simplify querying and analysis. It also advocates the use of Extract, Transform, Load (ETL) processes to populate the data warehouse. - Inmon Methodology:
The Inmon methodology, developed by Bill Inmon, takes a different approach by emphasizing the construction of a centralized enterprise data warehouse (EDW). According to this methodology, the EDW serves as a single source of truth, integrating data from various operational systems and transforming it into a consistent and reliable format. The Inmon methodology promotes a normalized data model, where data is organized into a set of subject-oriented, atomic-level tables. These tables are integrated through a data transformation layer, which handles the complexities of data integration. The Inmon methodology focuses on data integration, data quality, and data governance. It also supports the concept of data marts, which are derived from the enterprise data warehouse and serve specific departments or business units. - Data Vault:
The Data Vault methodology, developed by Dan Linstedt, is designed to address issues related to scalability, flexibility, and auditability. It is a hybrid approach that combines elements of both Kimball and Inmon methodologies. In the Data Vault methodology, the focus is on creating a flexible and scalable data model that can handle large volumes of data and changing business requirements. It uses a hub-and-spoke architecture, where the hub tables represent business entities, the satellite tables contain descriptive attributes, and the link tables capture the relationships between entities. The Data Vault methodology promotes a granular, historical approach to data storage, allowing for easy tracking of changes over time. It also emphasizes the use of automation and metadata-driven processes for building and managing the data vault.
In summary, Kimball methodology emphasizes dimensional modeling and simplicity, Inmon methodology focuses on building a centralized enterprise data warehouse, and Data Vault methodology aims at scalability, flexibility, and auditability through a hub-and-spoke architecture. The choice of methodology depends on the specific needs and requirements of the organization and the nature of the data being modeled.