Answer / Shailendra Singh

Hadoop MapReduce is a programming model for processing large datasets in parallel across a distributed system. It involves writing custom map and reduce functions to process the data. On the other hand, Apache Pig provides a higher-level abstraction for data analysis by using a language called Pig Latin which allows users to write dataflow programs without having to deal with intricate details of MapReduce programming.nnDifferences between Hadoop MapReduce and Pig:n1. Abstraction Level: MapReduce is a low-level system, while Pig provides a higher level of abstraction.n2. Programming Model: In MapReduce, you write custom map and reduce functions in Java or other languages, whereas in Pig, you write dataflow programs using the Pig Latin language.n3. Data Manipulation: MapReduce primarily focuses on data processing, while Pig allows for easier data manipulation, filtering, and joining of datasets.n4. Learning Curve: MapReduce has a steeper learning curve due to its low-level programming nature, whereas Pig is designed to be more user-friendly.

What is UDF?

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What do you know about the case sensitivity of apache pig?

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Explain tomap function?

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How to use 'foreach' operation in pig scripts?

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What are the different Relational Operators available in pig language?

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Explain the LOAD keyword in Pig script?

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Explain pig architecture?

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How should 'load' keyword is useful in pig scripts?

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What is a bag in pig?

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How will you merge the contents of two or more relations and divide a single relation into two or more relations?

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In which scenario Pig is better fit than MapReduce?

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Does Pig differ from MapReduce? If yes, how?

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