From the course: Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Cert Prep by Microsoft Press

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Preparing data for ingestion into a dimensional model

Preparing data for ingestion into a dimensional model - Microsoft Fabric Tutorial

From the course: Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Cert Prep by Microsoft Press

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Preparing data for ingestion into a dimensional model

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Preparing data for ingestion into a dimensional model. Now with a dimensional model, we're dealing with tables specified as dimension and fact tables. Now let's look at a couple of examples. So for my dimension load, I'm looking at denormalizing my data. So when I write, for example, my select query, or I can write a PySpark query, I may be looking at joining several source transaction tables together to form a single table. Now this speeds up querying, and it's a very common pattern in data warehouses and lake houses for analytical purposes. Now with my fact table, what I'm actually looking to do is go to my dimension tables to get my surrogate keys. Now my surrogate keys are generated in my dimension tables and they allow me to join from my fact table into my dimension table. So these two examples here are looking at ways in which we are denormalizing data to load a dimension by joining several tables together and preparing to load into a dimensional model. And for my facts load, I…

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