JSON is ubiquitous, particularly when working with APIs and logs. Its unstructured nature makes it highly flexible for handling anything from a simple array to a complex nested structure. However, this can also make it challenging for data analysis. When parsing JSON, it’s crucial to understand its structure so you can flatten it and convert it into a tabular format for analysis. Once the structure is identified, you can use pandas or PySpark to explode or normalize it into the desired shape. In this article, I will explain the method I use. While this approach is applicable to any notebook, there is a specific trick to make it work in a Fabric notebook.
Read on for that trick.