Most Frequently Used PySpark Window Functions | row_number | rank | lag | lead | Running Total

In this video, we cover the *most commonly used Window Functions in PySpark* with simple examples and easy Hinglish explanation. This topic is *very important for Data Engineer interviews* and real-time projects. ⏱️ Timestamps: 00:00 Introduction 00:22 Dataset used 00:44 row_number() explained 03:05 rank() explained 05:55 dense_rank() explained 06:56 lag() explained 09:52 lead() explained 11:26 Running Total explained 13:00 Summary & Interview Tips 💡 What you will learn: ✔ row_number() with example ✔ rank() vs dense_rank() difference ✔ lag() previous row comparison ✔ lead() next row comparison ✔ Running total (cumulative sum) ✔ Easy Hinglish explanation 🎯 This video is perfect for: PySpark Beginners Data Engineers Azure Data Engineers Databricks Users Interview Preparation 🚀 Frequently asked in interviews: Difference between row_number, rank, dense_rank When to use lag and lead How to calculate running total 👉 If you find this helpful: 👍 Like the video 💬 Comment your doubts 🔔 Subscribe for more PySpark interview content 📤 Share with your friends preparing for Data Engineering roles #PySpark #WindowFunctions #DataEngineering #Databricks #PySparkInterview #BigData #Spark #LearnPySpark #AzureDataEngineer #DataEngineer