Data warehouse models: examples and techniques | Vaultspeed
Hero templatewithout line 1
Examples, techniques and defintiton

Data warehouse modeling

Centralizing data to make it more accessible is only part of the solution. You also need to model it to translate complexity into meaningful insights and visualizations.

What is a data warehouse?

A data warehouse serves as a central repository of information, facilitating informed decision-making based on all relevant data. It is designed for data analytics, involving examining large amounts of data to understand relationships and trends.

By definition, it functions as a time-variant storage solution optimized to efficiently store data, minimizing input and output (I/O) for quick query results, catering to numerous business analysts, data scientists and decision makers concurrently.

While many companies rely on data warehouses, often cloud-based, users may also have alternatives like the data lakehouse, data fabric, or data mesh.

What is data warehouse modeling?

Data warehouse modeling encompasses the process of structuring and organizing data within a data warehouse to support effective business intelligence and decision-making.

It involves designing a predefined model that integrates various data sources into a cohesive and consistent framework.

At its core, data warehouse modeling aims to provide a comprehensive view of organizational data, by defining relationships and hierarchies among different data entities, such as customers, products, and transactions.

Data warehouse modeling methodologies and techniques

Inmon 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

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

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.

Benefits of a data warehouse model

  • Comprehensive view: Data warehouse models provide a comprehensive view, connecting the dots between different data entities.
  • Business-friendly presentation: Analysts can effectively communicate their findings by translating complex data into meaningful insights and visualizations.
  • Optimized for time-variant data: A data warehouse model is designed to allow analysts and managers to track changes and trends over time.
  • Ensures data quality and consistency: Data warehouse modeling plays a crucial role in establishing standardized data models and schemas and helps maintain data integrity and reliability.
  • Streamlines integration and transformation: The predefined nature of the model streamlines data integration and transformation processes, reducing complexity and improving efficiency in data warehouse operations.

Understand the automation potential of the different modeling approaches

Read this eBook to understand why Data Vault 2.0 excels in comparison to Inmon and Kimball.

Read the whitepaper

Understand the automation potential of the different modeling approaches

Read this eBook to understand why Data Vault 2.0 excels in comparison to Inmon and Kimball.

Read the eBook

Data Vault 2.0 repository

Want to find out more about Data Vault 2.0, following trainings or meeting like-minded professionals?

Explore now

Curious to learn more?