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Why Are JSON Files Converted to Flat Text Format Before MapReduce Processing in Hadoop?
Find out why JSON files must be converted to flat text format before processing in Hadoop MapReduce — a key concept for Big Data certification exams covering MapReduce, Pig, and Hive data pipelines and parsing techniques.
Question
What is the main reason JSON files are converted into flat text format before processing?
A. To reduce the data size drastically
B. To simplify parsing and enable easier processing in MapReduce
C. To remove all attribute values permanently
D. To make them readable in Microsoft Word
Answer
B. To simplify parsing and enable easier processing in MapReduce
Explanation
MapReduce processes input data line by line, treating each line as an individual record fed into the Mapper function. JSON files are inherently hierarchical and nested structures — meaning a single logical record can span multiple lines, making direct line-by-line parsing unreliable and complex. Converting JSON into a flat text format (such as tab-separated or comma-separated values) collapses each record into a single line, allowing MapReduce to correctly split, read, and process each entry without needing a custom multi-line parser. Option A is incorrect because flattening JSON does not drastically reduce data size — it can actually increase it slightly due to delimiter additions. Option C is wrong because attribute values are preserved, not removed; the structure is simply reorganized. Option D is entirely irrelevant to any Hadoop processing pipeline, as human readability in word processors has no bearing on distributed data processing.