Mastering PySpark:- 20 Essential SQL Interview Questions for Data Engineers

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As a seasoned Data Engineer with over six years of experience, I’ve encountered numerous challenges and solutions in the realm of big data processing. PySpark, a powerful tool for handling large datasets, often features prominently in technical interviews.

In this article, I’ll walk you through 20 frequently asked SQL and PySpark interview questions, providing detailed answers, code snippets, and business justifications to help you ace your next interview.

1. Optimizing PySpark DataFrame Operations

Question: How would you optimize a PySpark DataFrame operation that involves multiple transformations and is running too slowly on a large dataset?

Answer: To optimize PySpark DataFrame operations, consider the following strategies:

• Lazy Evaluation: PySpark uses lazy evaluation, which means transformations are not executed until an action is called. This allows PySpark to optimize the execution plan. Ensure that transformations are chained together before an action is called.
• Predicate Pushdown: Use filter operations early to reduce the amount of data processed.
• Column Pruning: Select only the necessary columns to minimize data shuffling.