Dimensional modeling is a data modeling technique that involves breaking the data into “facts” and “dimensions” to arrange& describe entities inside of your data warehouse. The outcome is a staging layer in the data warehouse that cleanses and sorts out the data, into the business end of the warehouse; making it accessible to data consumers.
By breaking your data into precisely defined and arranged entities, your consumers can understand what that data is, what it’s used for, and how to link it to additional data. Ultimately, using SQL Server Dimensional Modeling for your data allows for creating, the proper layer of models to expose in an end business intelligence (BI) tool.
Dimensional Modeling Use In Real Life
If you run a bakery without ever realizing it, you’re doing a form of dimensional modeling. What’s the ultimate output from a bakery? It’s that sparkling, glass display of sumptuous cupcakes, cakes, cookies, and all things in between. Nevertheless, a cupcake simply didn’t magically appear in the display case! Raw ingredients underwent a rigorous process of prepping, mixing, melting, and baking before they got there.
Like eating raw flour isn’t that palatable, neither is deriving insights from raw data as it hardly has a nice structure that makes it ready for analytics. There’s a lot of work that’s required to arrange data and make it usable for business users.
This is where dimensional modeling becomes usable. It’s a method that can assist data folks in creating meaningful entities like cupcakes and cookies, to dwell inside their data mart (your glass display). Then ultimately use for bi (business intelligence) purposes (eating the said cupcakes and cookies).
The benefits of using SQL Server dimensional modeling are pretty straightforward. Basically, the main advantages can be wound up as:
More accessibility: As the output of good dimensional modeling is a data mart, the tables being created are easier to decipher and more accessible to the end user.
More flexibility: Easy to slice, dice, filter, and view your data in whichever way befits your purpose.
Performance: Fact and dimension models are basically presented as tables or incremental models. As these typically form the core understanding of a business, they are queried frequently. Presenting them as tables enables them to be more performant in downstream BI platforms.
Few Flavors: Dimensional Modeling has a few flavors, however, the overall design is industry standard, so it is being used effectively for decades.
Centralized Implementation: Dimensional modeling allows centralized implementation of business logic; and persistent definitions across business users.For example, a single source of truth of customer definition.
Plug and Play:The design supports the ‘plug and play’ of new subject areas and actually the model becomes more powerful as more dimensions are added.
To Conclude
Dimensional data modeling is a data modeling technique that enables you to arrange your data into precise entities for mixing and matching in different ways. That can provide your stakeholders with plenty of flexibility. Although the exact approaches have changed and will continue to, the philosophical principle of having tables that are sources of truth; and tables that describe them; will always remain important in the work of analytics engineering practitioners.
