![]() ![]() As such, people typically liken facts to verbs. So what are these components? Facts Ī fact is a collection of information that typically refers to an action, event, or result of a business process. The ultimate goal of dimensional modeling is to be able to categorize your data into their fact or dimension models, making them the key components to understand. Dimensional modeling can be a method to get you part of the way there. So I guess we take it back-you’re not just trying to build a bakery, you’re also trying to build a top-notch foundation for meaningful analytics. This is where dimensional modeling comes into play it’s a method that can help data folks create meaningful entities (cupcakes and cookies) to live inside their data mart (your glass display) and eventually use for business intelligence purposes (eating said cookies). There’s some considerable work that’s needed to organize data and make it usable for business users. Just as eating raw flour isn’t that appetizing, neither is deriving insights from raw data since it rarely has a nice structure that makes it poised for analytics. But a cupcake just didn’t magically appear in the display case! Raw ingredients went through a rigorous process of preparation, mixing, melting, and baking before they got there. What’s the final output from a bakery? It’s that glittering, glass display of delicious-looking cupcakes, cakes, cookies, and everything in between. If you run a bakery (and we’d be interested in seeing the data person + baker venn diagram), you may not realize you’re doing a form of dimensional modeling. Not the answer you expected? Well, let’s open up our minds a bit and explore this analogy. This may come as a surprise to you, but we’re not trying to build a top-notch foundation for analytics-we’re actually trying to build a bakery. Let’s take a step back for a second and ask ourselves: why should you read this glossary page? What are you trying to accomplish with dimensional modeling and data modeling in general? Why have you taken up this rewarding, but challenging career? Why are you here? Here, we’ll focus on dimensional modeling from Kimball’s perspective-why it exists, where it drives value for teams, and how it’s evolved in recent years. Ralph Kimball’s work formed much of the foundation for how data teams approached data management and data modeling. ![]() ![]() The big hitters are the Kimball methodology and the Inmon methodology. There are a few different methodologies for dimensional modeling that have evolved over the years. Ultimately, using dimensional modeling for your data can help create the appropriate layer of models to expose in an end business intelligence (BI) tool. The result is a staging layer in the data warehouse that cleans and organizes the data into the business end of the warehouse that is more accessible to data consumers.īy breaking your data down into clearly defined and organized entities, your consumers can make sense of what that data is, what it’s used for, and how to join it with new or additional data. Dimensional modeling is a data modeling technique where you break data up into “facts” and “dimensions” to organize and describe entities within your data warehouse.
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