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What Key Advantage Makes Hadoop Ideal for Complaint Analysis Projects?
Hadoop’s distributed processing and fault tolerance handle massive complaint datasets for Pig/Hive analysis, ensuring reliable location/product insights—crucial advantage for Hive & Pig certification exam success.
Question
What is a key advantage of using Hadoop for this project?
A. It automatically generates customer reports
B. It processes only small data files
C. It provides distributed data processing and fault tolerance
D. It requires expensive high-end servers
Answer
C. It provides distributed data processing and fault tolerance
Explanation
A key advantage of using Hadoop for the Customer Complaint Analysis project is its distributed data processing across cluster nodes via HDFS for storage and MapReduce/YARN for parallel computation, enabling scalable analysis of massive retail complaint datasets that exceed single-server memory limits, while built-in fault tolerance through data replication, task retries, and speculative execution ensures reliable processing even during node failures or network issues common in petabyte-scale environments. This architecture supports Pig’s high-level ETL scripts for location/product grouping and Hive’s SQL queries on structured aggregates without data loss, delivering segmented insights like city-specific issue frequencies cost-effectively on commodity hardware, unlike traditional databases that falter under similar volumes.