Employing SQL and Dimensional Modeling To Optimize Datasets for BI tools

 Simply put, dimensional modeling is all about separating your data between entities and events pertaining to those entities. Three fundamental concepts are dimension tables, fact tables, and measures.

Fact tables: a stack of data that characterizes business events (a fact). It bears connections to measures and dimensions to pull out additional details.

Measures: numeric characteristics of the fact table. It may perhaps be calculations, or simply values pertaining to an event. Furthermore, helps with questions that can be answered via numbers for example: How much? At which rate?

Dimension tables: entities that reply to clear-cut business questions about the fact table: Who? What? Where? When? Why?

How can dimensional modeling help us to reduce datasets?

Easy! Imagine if you needed to get all of this data from a single source. It will be a large table, right?

With SQL server dimensional modeling you are compressing the amount of repeated information. As a result, you are freeing up space in your storage. Simply put - you are getting (1) augmented performance; (ii) cutting down storage costs and (iii) concocting an organized database structure where finding information on a specific subject is hassle-free.

Why do it via SQL instead of from the BI tool?

This is something tricky to define and that you may have searched for a long time. There’s no right or wrong answer, actually. However, you may have experienced, having a large dataset inserted in a BI tool. And treating it after importing is actually bad both with respect to performance and storage usage.

Moreover, will plausibly give you a heavy headache (and a few after-hour calls).

Doing it via SQL enables easy adaptation to various BI tools.

If you work in an enterprise that uses several different reporting tools, you got the point already. Now and then you need to migrate a report or dashboard to a different one. And alas doing it from scratch takes a bit of time!SQL server dimensional modeling makes the job simple.

If a star schema is already there, for instance, ready to use in your database, then your migration is near about done. Just import them to your BI tool and voila! Just utilize your time now, to build the charts and improve the UX.

Conclusion

Since this reduction is what makes your dataset fast and pruned, if you understand the precise timeframes your stakeholders will need, you’ll be sufficing all needs, with fewer data and greater performance.