PySpark Interview Question | Flatten Nested Data using explode() | Real Databricks Demo

PySpark Interview Question | Flatten Nested Data using explode() | Real Databricks Demo In this video, we solve one of the most common PySpark interview questions — how to flatten nested data using the explode() transformation. When working with real-world data from APIs, Kafka, or NoSQL systems, data often comes in nested JSON format. As a Data Engineer, you must know how to convert this into a flattened, usable structure. In this video, you’ll learn: What is explode() in PySpark How to flatten array columns step by step Difference between explode() and explode_outer() Handling nulls and empty arrays (important interview edge case) Hands-on demo using Databricks Free Edition This is part of my PySpark Coding Interview Series, where I focus on real interview questions + practical solutions. 💻 Code & Resources 📂 GitHub Repository: 👉 https://github.com/mahesh-cr-de/pyspark-coding-interview 🎯 Who is this for? Data Engineers preparing for interviews Spark / PySpark developers Anyone working with JSON / nested data 🔥 Topics Covered PySpark explode function Flatten nested JSON Handling arrays in Spark Interview tips & common mistakes 🚀 About This Series In this playlist, I cover: ✔ Real PySpark interview questions ✔ Step-by-step explanations ✔ Databricks hands-on demos ✔ Production-level insights #pysparktutorial #explode #flatten #databrickstutorial #azuredatabricks #dataengineering