The Dimensional Modeling & Granularity Edition

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As a Data Engineer/Analytics Engineer, one of the most common questions I get asked is, “How do you decide what data goes into the warehouse/model when you’re starting out?” It’s a great question, and the answer is both simple and complex: I go as granular and broad as I can.

Why, you ask? Oh boy, let me tell you!

When I say I go as granular (or atomic) as possible, I mean that I dive to the most granular level of the data that I can. This approach allows me to not only meet the immediate needs and answer the specific questions that were asked of me, but also to anticipate and prepare for additional questions that might come up later. It’s all about being ready for the things we don’t know we don’t know.

By capturing data at the most detailed level, I can ensure that my model is flexible and scalable. This means that as new questions arise, I (or self-service users) can easily drill down into the data to find the answers without having to go back and rework the model. It’s like having a Swiss Army knife of data – ready for any situation!

Dimensional models are highly scalable, and the devil is truly in the details. And, you know where the details are… in your data! The more detailed your data, the more powerful your analysis can be. Think of it like building a house: the stronger and more detailed the foundation, the more robust and versatile the house will be.

But why is dimensional modeling the most viable and accepted technique for delivering data for data warehouses and business intelligence? Let’s dive into that.

• Ease of Use: Dimensional models are designed to be user-friendly, making it easier for business users to understand and navigate the data 
• Efficient Query Performance: They are optimized for high-performance querying, which is crucial for business intelligence applications that require quick and efficient data retrieval 
• Conformed Dimensions: These allow for drilling across different business processes, providing a unified view of the data 
• Scalability and Performance: Dimensional models support high concurrency and scalability, making them suitable for large-scale data warehousing and business intelligence applications 
• Flexibility: The structure of dimensional models allows for easy extension and adaptation as business needs change, ensuring that the data warehouse can grow and evolve over time 
• Proven Methodology: Dimensional modeling has been a settled debate in the industry, with established best practices and methodologies that have been refined over decades 

  Using separate fact tables for data at different levels of granularity is generally the best practice in dimensional modeling. This approach helps maintain a clear and accurate data model, making it easier to manage and analyze your data effectively.

  So, next time you’re starting a new Power BI project, remember to go granular and broad (but do not shove all levels of granularity into one table – I beg you!). You’ll thank yourself later when you’re able to answer not just the questions you have now, but also the ones you haven’t even thought of yet.

  Happy data modeling!