Q. Which two major steps constitute the MapReduce model?

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Q. What type of results is generated during the MapReduce process?

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Q. How does MapReduce ensure scalability and reliability?

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Q. What is the role of the master node in MapReduce?

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Q. How does MapReduce compare to Relational Database Management Systems (RDBMS)?

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Q. What is a key difference between MapReduce and RDBMS in terms of data processing?

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Q. Why is MapReduce considered more suitable for handling larger data sets compared to traditional RDBMS?

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Q. What is one of the criticisms of MapReduce by RDBMS proponents?

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Q. How are MapReduce and RDBMS described in terms of their relationship?

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Q. What are the primary inspirations for Distributed Key-Value and Column-oriented DBMS?

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Q. How do Distributed Key-Value and Column-oriented DBMS differ from traditional databases?

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Q. What distinguishes Grid computing from MapReduce in terms of data processing?

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Q. What advantage does MapReduce offer to programmers compared to Grid computing?

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Q. Which popular column-oriented DBMS uses its own implementation of MapReduce internally?

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Q. What is a typical use case for shared-memory parallel programming environments like OpenMP?

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Q. How do shared-memory parallel programming interfaces like OpenMP compare to MapReduce in terms of flexibility and ease of use?

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Q. In the MapReduce model, what is the purpose of the map() function?

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Q. How are key-value pairs processed in the reduce() function in the MapReduce model?

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Q. In the MapReduce word count application, what is the purpose of the map() function?

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Q. What is the primary task of the reduce() function in the word count application?

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