Pyspark interview questions for Data Engineer

1. How do you handle data transfer between PySpark and external systems? 2. How do you deal with missing or null values in PySpark DataFrames?
3. Are there any specific strategies or functions you prefer for handling missing data?
4. What is broadcasting, and how is it useful in PySpark?
5. What is Spark and why is it preferred over MapReduce?
6. How does Spark handle fault tolerance?
7. What is the significance of caching in Spark?
8. Explain the concept of broadcast variables in Spark
9. What is the role of Spark SQL in data processing?
10. How does Spark handle memory management?
11. Discuss the significance of partitioning in Spark.
12. Explain the difference between RDDs, DataFrames, and Datasets.
13. What are the different deployment modes available in Spark?
14. What is PySpark, and how does it differ from Python Pandas?
15. Explain the difference between RDD, DataFrame, and Dataset in PySpark. 16. How do you create a DataFrame in PySpark?
17. What is lazy evaluation in PySpark and why is it important?
18. How can you handle missing or null values in PySpark DataFrames?
19. What are transformations and actions in PySpark, and can you give examples of each?
20. How do you perform joins between two DataFrames in PySpark? What are the joins available in PySpark?

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