Mastering PySpark: Insights from a Senior Data Engineer’s Interview Experience

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As a seasoned big data engineer with over a decade of experience in Python, PySpark, Spark, and AWS cloud computing, I recently had the opportunity to participate in a series of technical interviews for a senior PySpark role. The interviewers were particularly impressed by my in-depth understanding of PySpark, big data, SQL, and data warehousing.

In this article, I’ll share the most challenging questions I encountered and provide comprehensive answers that showcase the practical application of PySpark in real-world scenarios.

Introduction

1. Can you provide an overview of your experience working with PySpark and big data processing?

Throughout my 6-year career, I’ve had the privilege of working with PySpark and big data processing across various industries and use cases. I’ve architected and implemented large-scale data pipelines, real-time streaming solutions, and advanced analytics platforms using PySpark as the core technology.

One of my most significant projects involved building a real-time recommendation engine for a major e-commerce platform. We processed terabytes of user interaction data daily using PySpark, enabling personalized…