Answer / Arvind Chaurasia

Apache Spark has several key features that make it popular for big data processing:n
1. Speed: Apache Spark is significantly faster than other big data processing systems like Hadoop MapReduce, due to its in-memory caching and efficient task scheduling.n
2. Scalability: Apache Spark can scale out to handle large amounts of data by distributing tasks across multiple nodes in a cluster.n
3. Simplicity: Apache Spark has a simple and easy-to-use programming API, which makes it accessible to developers with various skill levels.n
4. Integration: Apache Spark can integrate with various big data storage systems like HDFS, Cassandra, and MongoDB.n
5. Real-time processing: Apache Spark can process streaming data in real-time, making it useful for real-time analytics and machine learning applications.

Name the two types of shared variable available in Apache Spark?

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Explain Spark coalesce() operation?

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What is an rdd?

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What does it mean by Columnar Storage Format?

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What is spark accreditation?

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What are the ways to create RDDs in Apache Spark? Explain.

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How is RDD in Apache Spark different from Distributed Storage Management?

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What are transformations in spark?

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Explain benefits of lazy evaluation in RDD in Apache Spark?

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List the benefits of Spark over MapReduce.

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What are the limitations of Spark?

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What do you understand by Pair RDD?

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