- What is PySpark and how is it different from Apache Spark?
- What is the role of a SparkContext in PySpark?
- How does PySpark handle parallel processing of large data sets?
- What is a Resilient Distributed Dataset (RDD) in PySpark and how is it used?
- How does PySpark handle data persistence and caching?
- What is the difference between map and flatMap in PySpark?
- What is the use of Spark SQL in PySpark?
- How does Spark Streaming work in PySpark and what are some of its use cases?
- What are some of the most common optimization techniques for PySpark applications?
- Can you explain how to monitor and profile PySpark applications to identify performance bottlenecks?
- How does PySpark handle data partitioning and shuffling?
- What are some of the most common data sources supported by PySpark?
- What is the use of Spark MLlib in PySpark and what are some of its most popular algorithms?
- How can you deploy a PySpark application on a cluster?
- Can you explain how to use broadcast variables in PySpark and why they are useful?
- How does PySpark handle data visualization and reporting?
- Can you give an example of using PySpark to perform data transformation and data wrangling tasks?
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