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What Data Powers Location-Based Analysis in Hadoop Complaint Projects?
Hadoop Customer Complaint projects analyze complaint records grouped by customer location using Pig GROUP BY and Hive queries, generating geospatial insights for business optimization—key for Hive & Pig certification success.
Question
What type of data does the project use for location-based analysis?
A. Real-time GPS tracking data
B. Product sales by warehouse
C. Image and video data
D. Complaint records grouped by customer location
Answer
D. Complaint records grouped by customer location
Explanation
The Customer Complaint Analysis project uses complaint records grouped by customer location—typically semi-structured text data containing fields like complaint ID, timestamp, location (city/region/store), issue category, description, and resolution status—for location-based analysis via Hadoop tools like Pig and Hive. Pig scripts perform GROUP BY operations on location fields to aggregate issue frequencies, enabling dynamic filtering by user-specified cities through command-line parameters, while Hive tables store partitioned results for SQL queries revealing patterns such as high defect rates in specific regions or slow response times in urban stores. This geospatial grouping transforms raw retail complaints stored in HDFS into actionable, segmented reports that pinpoint operational weaknesses by geography, supporting targeted interventions without requiring real-time GPS, sales transactions, or multimedia data processing.