TOP PYSPARK INTERVIEW QUESTION 2023

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  1. What is Apache Spark and how does it differ from Hadoop?
  2. What are the benefits of using Spark over MapReduce?
  3. What is a Spark RDD and what operations can be performed on it?
  4. How does Spark handle fault-tolerance and data consistency?
  5. Explain the difference between Spark transformations and actions.
  6. What is a Spark DataFrame and how is it different from an RDD?
  7. What is Spark SQL and how does it work?
  8. How can you optimize a Spark job to improve its performance?
  9. How does Spark handle memory management and garbage collection?
  10. Explain the role of Spark Driver and Executors.

  11. What is PySpark and how does it differ from Apache Spark?

  12. How do you create a SparkContext in PySpark? What is the purpose of SparkContext?

  13. What is RDD (Resilient Distributed Dataset)? How is it different from DataFrame and Dataset?

  14. What are the different ways to create RDD in PySpark?

  15. What is the use of persist() method in PySpark? How does it differ from cache() method?

  16. What is the use of broadcast variables in PySpark? How are they different from normal variables?

  17. What are the different types of transformations and actions available in PySpark?

  18. What is a SparkSession in PySpark and how is it different from SparkContext?

  19. What is a partition in PySpark? How do you change the number of partitions in an RDD?

  20. What is the use of Spark UI in PySpark? How do you access it?

  21. What are the different deployment modes available in PySpark?

  22. What is the use of checkpointing in PySpark? How do you enable checkpointing?

  23. What is a shuffle in PySpark? How does it impact the performance of a PySpark job?

  24. What is the use of PySpark MLlib? What are some common algorithms available in MLlib?

  25. How do you optimize a PySpark job for better performance? What are some best practices for PySpark optimization?

  26. What is PySpark, and what are its key components?

  27. What is the difference between map and flatMap in PySpark?\

  28. How do you persist RDDs in PySpark, and why is it important?

  29. What is a SparkSession, and how is it created in PySpark?

  30. What is the difference between DataFrame and RDD in PySpark?

  31. How do you optimize the performance of PySpark jobs?

  32. What is a shuffle in PySpark, and how does it impact job performance?

  33. How do you perform machine learning tasks in PySpark?
    What is the role of PySpark in big data analytics?

  34. How do you handle missing or null values in PySpark data frames?

  35. What are the differences between RDD, Dataframe, and Dataset in PySpark?

  36. How do you create a PySpark application?

  37. How do you read data from a CSV file in PySpark?

  38. How do you perform data cleansing in PySpark?

  39. How do you join two DataFrames in PySpark?

  40. What is SparkContext and what is its significance in PySpark?

  41. How do you handle missing or null values in PySpark?

  42. How do you optimize a PySpark job?

  43. How do you use PySpark with SQL?

  44. How do you handle skewed data in PySpark?

  45. How do you broadcast variables in PySpark?

  46. How do you use PySpark to process streaming data?

  47. How do you debug a PySpark application?

  48. How do you implement machine learning algorithms in PySpark?

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