Apache Spark Data Transformation: Flattening Structs & Exploding Arrays

In one of my project involving data engineering, I was faced with the task of handling JSON files. These files contained not just primitive data types, but also reference data types (arrays and structs). The project required me to read these JSON files, flatten their structure, and then save the data in a CSV format. This essentially meant that I had to convert all reference data types into primitive ones. The main challenge in this task was the overwhelming number of columns that contained reference data types. Given the large number of such columns, manually flattening these reference data types was not a feasible solution. To overcome this challenge, I have created a function with the help of <a href="https://medium.com/@nalin.rs/new-avatar-of-chatgpt-35fa5ee747f2" rel="noopener">Bing Chat</a> that could dynamically convert all reference data types into primitive ones. This function utilized an explode mechanism to flatten the structure, effectively simplifying the task and making it more efficient. <a href="https://medium.com/@nalin.rs/apache-spark-data-transformation-flattening-structs-exploding-arrays-0c4db948acce">Click Here</a>