The Past, Present, and Future of Data Modelling Techniques in Modern Data Warehouse
Data warehousing has become an essential tool for businesses looking to leverage their data to gain insights and make better decisions. One of the key components of data warehousing is data modeling, which involves designing a data structure that can be used to store and analyze data effectively. Over the years, there have been several data modeling techniques, each with its own strengths and weaknesses. In this article, we will explore the evolution of data modeling techniques, from the early days of data warehousing to the emergence of Data Vault 2.0.
Early Data Modeling Techniques
In the early days of data warehousing, the most common data modeling techniques were the star schema and the snowflake schema. These techniques were designed to handle structured data and provided a way to organize data in a way that could be easily understood by business users. The star schema, in particular, was popular because it was simple and easy to understand. However, as data volumes grew and data structures became more complex, these techniques proved to be inadequate.
The Emergence of Data Vault
In the early 2000s, a new data modeling technique emerged: Data Vault. Developed by Dan Linstedt, Data Vault was designed to handle complex data structures and provide greater flexibility than traditional data modeling techniques. Data Vault uses a hub-and-spoke architecture, which allows for easy scalability and adaptability. The hub represents a business concept, such as a customer or a product, while the spokes represent relationships between the hubs. The Data Vault model is built around three types of tables: hub tables, link tables, and satellite tables. This allows for better traceability and a more comprehensive view of the data.
Data Vault 2.0
In recent years, Data Vault has continued to evolve, with the latest version being Data Vault 2.0. Data Vault 2.0 introduces several new concepts, such as the Business Vault, which provides a business-oriented view of the data, and the use of data vault automation tools, which simplify the process of building and maintaining a Data Vault model. Data Vault 2.0 also includes a number of best practices and guidelines, which ensure that the model is optimized for performance, scalability, and maintainability.
According to Databrick :
A Data Vault is a more recent data modeling design pattern used to build data warehouses for enterprise-scale analytics compared to Kimball and Inmon methods.
Data Vaults organize data into three different types: hubs, links, and satellites. Hubs represent core business entities, links represent relationships between hubs, and satellites store attributes about hubs or links.
What are the benefits of Data Vault in digital transformation?
- delivers the modernised data service needed by a digital transformation program
- ensures that your data service is economical to run and achieves considerably improved productivity
- enables new business capability such as data-driven decision-making, data science, and can be the key to new business models
- contributes to organisational agility, improving the speed at which the business can learn about and exploit opportunities or counter threats
Of all these, organisational agility creates the most business value and is a major contributor to the success of any digital transformation.
How Data Vault 2.0 Has Changed the Game
Data Vault 2.0 has changed the game for data warehousing by providing a more scalable, adaptable, and flexible approach to data modeling. By using Data Vault 2.0, businesses can handle large volumes of data, adapt to changing business requirements, and achieve a more comprehensive view of their data. Data Vault 2.0 also provides a more efficient way to build and maintain a data model, reducing the time and cost involved in the data warehousing process.
Conclusion
Data modeling is a critical component of data warehousing, and over the years, there have been several data modeling techniques that have emerged. From the early days of data warehousing to the emergence of Data Vault 2.0, data modeling techniques have continued to evolve, providing businesses with a more scalable, adaptable, and flexible approach to data modeling. By using Data Vault 2.0, businesses can gain a more comprehensive view of their data, handle large volumes of data, and achieve better insights and decision-making capabilities.
