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Which Hadoop Component Handles Batch Processing of Large Datasets?
MapReduce powers Hadoop’s batch processing for massive datasets like customer complaints, splitting jobs into parallel map/reduce phases across HDFS—vital for Hive & Pig certification projects analyzing location-based insights.
Question
Which component of Hadoop enables batch data processing for large-scale datasets?
A. Ambari
B. Sqoop
C. MapReduce
D. HiveServer2
Answer
C. MapReduce
Explanation
MapReduce is the core Hadoop component that enables batch data processing for large-scale datasets by dividing complex computations into parallel map and reduce phases executed across cluster nodes, handling petabyte-scale data through automatic fault tolerance, data locality optimization, and scalable shuffling of intermediate key-value pairs stored in HDFS. In the Customer Complaint Analysis project, MapReduce underlies Pig and Hive operations, processing retail complaint records via mappers that parse and emit location-complaint tuples, followed by reducers that aggregate issue frequencies per region—delivering segmented reports without loading entire datasets into memory, making it ideal for non-interactive, high-throughput ETL workloads in certification scenarios.