Welcome to the second part of a 5-part series on an end-to-end use case for Microsoft Fabric. This post will focus on the data engineering part of the use case. In this series, we will explore how to use Microsoft Fabric to ingest, transform, and analyze data using a real-world use case.
I’m going to be honest with you. I’m bad at writing Pandas data transformation code. Throughout the years I mostly focussed on the Spark APIs in Scala and PySpark, SQL, dbt, and some others, but I find the Pandas APIs usually just confusing and hard to read. I don’t like the black box magic and lock-in of low-code solutions either. Did you know that Microsoft Fabric has the perfect middle ground for this? It’s called Data Wrangler. Let’s dive in!