One of the strengths of Microsoft Fabric is how easily it handles different data formats within a single notebook. Whether your data arrives as a CSV file, a JSON file, an XML file, or anything in between, PySpark (or Python) gives you a consistent way to read, transform, and combine it. This makes Fabric an ideal environment for building data pipelines without relying on complex integrations.

In this example, I’ve loaded three completely different data file types:

  • .csv
  • .json
  • .xml

Each data file is read using a simple PySpark command, creating DataFrames that can be joined together just like any other structured dataset. I’ve also generated a date dimension directly in the notebook to show how straightforward it is to create a supporting dimension without needing external sources.

Once the files are loaded into a DataFrame, PySpark makes it easy to clean data, standardise data types, and create consistent join keys. From there, everything behaves as a single combined dataset. I can slice and dice my “fact” sales by any one of my “dimensions”, Product, Store or Date.

The benefit is clear: Fabric notebooks give you the flexibility to ingest and integrate data from almost any data format using one consistent approach. Whether you’re prototyping a pipeline or building a production ready model, PySpark notebooks in Microsoft Fabric provide a quick and powerful method to combine data from multiple sources.

Please see below my supporting documents if you want to have go yourself.

Pyspark Supporting Documents

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