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Why Use Pig for Big Data Processing in Hive and Pig Certification Projects?
Understand why Pig is the Hadoop tool for processing large customer complaint datasets in Hive & Pig projects—learn its Pig Latin scripting for efficient ETL, MapReduce optimization, and actionable insights in certification exams.
Question
Which Hadoop tool is used to process large datasets in this project?
A. Sqoop
B. Spark
C. Pig
D. MapReduce
Answer
C. Pig
Explanation
In the Customer Complaint Analysis project of the Hadoop Projects: Analyze Big Data with Hive & Pig certification, Pig serves as the key tool for processing large datasets of retail customer complaint records stored in HDFS. Its procedural scripting language, Pig Latin, facilitates high-level data transformations such as loading raw complaint data, filtering by location or category, grouping for aggregation, and applying user-defined functions (UDFs) for sentiment analysis—all compiled into optimized MapReduce jobs automatically. This approach streamlines ETL workflows compared to low-level MapReduce coding, integrates with Hive for structured querying, and handles the project’s petabyte-scale semi-structured data to derive insights on complaint trends, enabling faster iteration and business improvements.