Скачать или смотреть #19 Window Functions In PySpark Continued ntile,cume dist and percent rank | Arun Kumar

*"Window Functions in PySpark Continued: ntile, cume\_dist, and percent\_rank":

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Window Functions in PySpark - Continued | ntile, cume\_dist & percent\_rank Explained.

Welcome to another insightful session on *PySpark Window Functions* by *Arun Kumar* at **ForumDE**! In this video, we dive deeper into the power of window functions in PySpark with a focus on three essential analytical tools: **ntile**, **cume\_dist**, and **percent\_rank**.

These functions are widely used in real-time data engineering scenarios for advanced ranking, distribution analysis, and percentile calculations. Understanding how they work can help you gain a deeper perspective on your datasets and optimize complex queries in big data environments.

What You'll Learn:

✅ What is `ntile` and how it divides data into equal parts
✅ How `cume_dist` calculates the cumulative distribution of values
✅ How `percent_rank` helps rank rows in percentage terms
✅ Real-world use cases and live examples in PySpark
✅ Practical code walkthroughs and output interpretation

Whether you're preparing for **data engineering interviews**, working on **big data projects**, or just looking to improve your **PySpark skills**, this video is a must-watch!

🧑‍🏫 About the Instructor:

*Mr. Arun Kumar* is a highly experienced Data Engineering mentor with over 10 years in the industry, known for his practical teaching style and deep knowledge of the Azure and Big Data ecosystem.

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