TOP PYSPARK INTERVIEW QUESTION 2023

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What is Apache Spark and how does it differ from Hadoop? What are the benefits of using Spark over MapReduce? What is a Spark RDD and what operations can be performed on it? How does Spark handle fault-tolerance and data consistency? Explain the difference between Spark transformations and actions. What is a Spark DataFrame and how is it different from an RDD? What is Spark SQL and how does it work? How can you optimize a Spark job to improve its performance? How does Spark handle memory management and garbage collection? Explain the role of Spark Driver and Executors. What is PySpark and how does it differ from Apache Spark? How do you create a SparkContext in PySpark? What is the purpose of SparkContext? What is RDD (Resilient Distributed Dataset)? How is it different from DataFrame and Dataset? What are the different ways to create RDD in PySpark? What is the use of persist() method in PySpark? How does it differ from cache() method? What is the use of broadcast variables in PySpark
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In which case we can use orc or in which case we can use parquet ?

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The decision to use ORC or Parquet can depend on several factors, including the specific requirements of your use case and the tools and technologies that you are using. Here are some common scenarios where ORC or Parquet might be a better choice:

Use cases for ORC:

  • Complex data types or a large number of nested structures: ORC is designed to handle complex data types efficiently, making it a good choice for datasets with a large number of nested structures, arrays, and maps.
  • Low-latency queries: ORC is optimized for low-latency queries and can quickly access specific columns, making it a good choice for real-time or interactive querying scenarios.
  • Apache Hive ecosystem: ORC was developed by the Apache Hive team and is optimized for use with Hive, making it a good choice if you are using Hive for data processing and analysis.

Use cases for Parquet:

  • Large datasets with simpler schema: Parquet is efficient for storing and querying large datasets with simpler schema, making it a good choice for data warehousing and analytics scenarios.
  • Performance optimization: Parquet is optimized for high-performance reads and writes, and is highly scalable, making it a good choice for processing large amounts of data quickly.
  • Tool compatibility: Parquet is supported by a wider range of tools, including Apache Spark, Apache Impala, and Apache Arrow, making it a good choice if you are using these tools in your data processing pipeline.

Ultimately, the best way to determine which format to use is to test both formats with your data and analyze the performance and efficiency of each format for your specific use case.

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