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Why Is JSON-to-Flat-File Conversion Important for MapReduce Processing in Hadoop?
Discover why converting JSON to a flat-file format is a crucial step in Hadoop data pipelines. Learn how flattening simplifies parsing and enables efficient MapReduce processing for your Big Data certification exam.
Question
Why was the JSON-to-flat-file conversion step important?
A. To simplify parsing and enable MapReduce processing
B. To compress the dataset
C. To remove all values for privacy
D. To make it compatible with Excel macros
Answer
A. To simplify parsing and enable MapReduce processing
Explanation
The JSON-to-flat-file conversion step is critical in Hadoop workflows because MapReduce is fundamentally designed to process data line by line using standard text input formats. JSON files are hierarchical and multi-lined, meaning a single logical record (like a user’s profile) often spans across multiple lines, making it extremely difficult for the default MapReduce TextInputFormat to split and parse the records accurately without breaking the data structure. By flattening the JSON into a single-line format (such as CSV or tab-delimited text), each record is neatly contained on one line, allowing MapReduce to easily read, split, and process the data at scale.
Option B is incorrect because flattening JSON often does not compress it; in fact, adding delimiters can sometimes maintain or increase the file size. Option C is wrong because the goal is to restructure the values, not delete them for privacy. Option D is irrelevant, as Hadoop processes data for distributed computing, not for local Excel macro compatibility.