Explain Spark Execution and Optimization
Company: Point72
Role: Data Engineer
Category: Software Engineering Fundamentals
Difficulty: hard
Interview Round: Technical Screen
You are interviewing for a Data Engineer role. Answer the following Apache Spark and PySpark fundamentals questions in a practical production-data context:
1. What does it mean that Spark operations are lazy?
2. How does Spark perform distributed data processing across a cluster?
3. How would you optimize slow or expensive Spark/PySpark code? Discuss execution plans, shuffles, joins, partitioning, caching, file layout, skew, and streaming considerations where relevant.
Quick Answer: The question evaluates a candidate's understanding of Apache Spark's execution model and lazy evaluation, distributed data processing across clusters, and practical performance optimization concepts such as execution plans, shuffles, joins, partitioning, caching, file layout, skew, and streaming.