pyspark interview questions

      Comments Off on pyspark interview questions
  1. What is PySpark and how is it different from Apache Spark?
  2. What is the role of a SparkContext in PySpark?
  3. How does PySpark handle parallel processing of large data sets?
  4. What is a Resilient Distributed Dataset (RDD) in PySpark and how is it used?
  5. How does PySpark handle data persistence and caching?
  6. What is the difference between map and flatMap in PySpark?
  7. What is the use of Spark SQL in PySpark?
  8. How does Spark Streaming work in PySpark and what are some of its use cases?
  9. What are some of the most common optimization techniques for PySpark applications?
  10. Can you explain how to monitor and profile PySpark applications to identify performance bottlenecks?
  11. How does PySpark handle data partitioning and shuffling?
  12. What are some of the most common data sources supported by PySpark?
  13. What is the use of Spark MLlib in PySpark and what are some of its most popular algorithms?
  14. How can you deploy a PySpark application on a cluster?
  15. Can you explain how to use broadcast variables in PySpark and why they are useful?
  16. How does PySpark handle data visualization and reporting?
  17. Can you give an example of using PySpark to perform data transformation and data wrangling tasks?