In 1996, Ralph Kimball introduced the world to dimensional modeling for building data warehouses. Designed to optimize databases for storage and faster data retrieval, the bottom-up approach became quite popular. Thus, organizations increasingly started using a dimensional data model to design data warehouse architecture.
Dimensional Modeling in the Age of Modern Analytics
Dimensional schemas have withstood the test of time and can still handle granular data with efficiency. The focus of a dimensional approach has always been on performance, integration, and extensibility, and it continues to deliver on all of these fronts.
A dimensional data model allows enterprises to organize data into coherent business categories, making it easier for users to navigate databases. The models are deformalized and optimized for data querying. Here are some key selling points of dimensional modeling:
Today, users want to access and visualize the same datasets using multiple BI and query tools. Dimensional modeling helps with that as one of the core ideas behind it is that business users need to query data in various ways.
A dimensional data model allows easy integration among business processes. For example, an employee dimension allows human resource, sales, and finance departments to have one employee reference, irrespective of the source application.
A dimensional data model also offers great scalability. They allow organizations to add new data and modify existing tables without requiring significant changes.
Using slowly changing dimensions (SCDs), data modelers can store and manage current and historical data over time in a data warehouse. It’s the crux to tracking changes in data.
Analytical vs. Transactional Systems
A constellation of business Intelligence (BI) tools have emerged, contending that data modeling isn’t even necessary anymore. Some even claim to import fully normalized datasets from online transaction processing (OLTP) systems to support analytics and BI.
But they fail to deliver data in a consistent conceptual way like dimensional models, mainly at the enterprise level. The reason is that OLTP systems are not designed to support complex queries. Also, these systems don’t maintain aggregated historical data and contain highly normalized datasets.
Therefore, OLTP systems should be used to support online analytical processing (OLAP) systems primarily designed and optimized for conducting complex data analysis.
Dimensional modeling is still relevant — in fact, it’s far from obsolete. As the data landscape becomes more extensive and complex, dimensional modeling will continue to serve as an effective approach to accessing and utilizing data to gain insights.
Here’s how Astera DW Builder automated dimensional modeling feature can accelerate and simplify data warehousing: