Answer / Ram Prakash

Apache Spark processes data much faster than traditional MapReduce in Hadoop due to several reasons:n1) In-Memory Processing: Spark can cache data in memory, reducing disk I/O and improving processing speed.n2) Lazy Evaluation: Transformations in Spark are not executed immediately but only when an action is triggered, reducing overhead.n3) Faster Shuffle: Spark's shuffle operation is faster due to better sorting and merging algorithms.

Does Spark provide the storage layer too?

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How can you store the data in spark?

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Does spark replace hadoop?

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What are common uses of Apache Spark?

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Which one will you choose for a project –Hadoop MapReduce or Apache Spark?

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Why is there a need for broadcast variables when working with Apache Spark?

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State the difference between Spark SQL and Hql

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Explain about the different cluster managers in Apache Spark

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Does diesel engine have spark plug?

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To use Spark on an existing Hadoop Cluster, do we need to install Spark on all nodes of Hadoop?

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What is driver memory and executor memory in spark?

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What does MLlib do?

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