Data warehouse modeling

Inmon modeling, named after Bill Inmon, is a data modeling methodology that emphasizes the creation of a centralized data warehouse. In this approach, data is integrated from various sources into a single, coherent structure, often referred to as a "single version of truth."

The primary focus is on normalization, which involves organizing data into tables to minimize redundancy and improve data consistency. Inmon modeling typically follows the conceptual-to-logical-to-physical enterprise data warehouse (EDW) model approach,, and then data marts are derived from it to serve specific business needs.

One of the key advantages of Inmon modeling is its ability to provide a comprehensive view of organizational data, making it suitable for complex and large-scale enterprises. Maintaining a centralized data repository facilitates better decision-making by ensuring consistency and accuracy across the organization.

Unfortunately, Inmon modeling was not initially designed for data warehousing, lacking compatibility with BI frontends and resulting in complexities such as joining data across timelines, loading data in specific orders, and managing cascading changes, often requiring extensive modeling efforts to prevent disruptions.

Kimball modeling, developed by Ralph Kimball, is an alternative approach to data modeling that focuses on building data marts to serve specific business requirements.

Unlike Inmon modeling, Kimball modeling follows a bottom-up approach, where data marts are created first and then integrated into a data warehouse. This methodology prioritizes simplicity and flexibility, aiming to deliver quick and tangible results to end users.

One of the main strengths of Kimball modeling lies in its agility and responsiveness to changing business needs. By organizing data into dimensional models such as star schemas and snowflake schemas,

Kimball modeling makes it easier for business users to understand and query the data. This approach is particularly well-suited for organizations with dynamic and evolving requirements, as it allows for incremental development and rapid deployment of data marts.

However, Kimball modeling lacks an integrated enterprise information model, has to rely on sometimes unclear user-driven requirements, struggles with real-time aggregation processing, and late arriving dimension records.

Data Vault modeling is a data modeling methodology designed to address the challenges of agility, scalability, and auditability in data warehouse environments.

Developed by Dan Linstedt, Data Vault modeling emphasizes the creation of a highly scalable and flexible data architecture composed of three types of tables: Hubs, Links, and Satellites. Hubs represent business entities, Links capture relationships between entities, and Satellites store historical and contextual data.

One of the key advantages of Data Vault modeling is its ability to accommodate changes and scale seamlessly as data volumes grow. The modular nature of Data Vault structures enables easy integration of new data sources without disrupting existing components.

Additionally, Data Vault modeling provides comprehensive traceability and auditability, making it well-suited for industries with stringent regulatory requirements such as finance and healthcare.

Data Vault modeling is considered a superior choice for data warehouse automation because of its efficiency, maintainability, and adaptability benefits.

And do know that Kimball modeling works perfectly on top of a Data Vault.