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What Challenge Occurs When Parsing Income Fields in MapReduce Big Data Projects?
Learn about the common challenges of parsing income fields in Hadoop MapReduce. Discover how spaces and unexpected characters cause Number Format Exceptions and how to fix them for your Big Data certification exam.
Question
What challenge occurred when parsing income fields?
A. Number format exceptions due to spaces in data
B. Too many files in HDFS
C. Incomplete Hadoop installation
D. Incorrect SQL queries
Answer
A. Number format exceptions due to spaces in data
Explanation
When parsing income fields in Hadoop or Java-based MapReduce jobs, a very common and highly disruptive challenge is encountering NumberFormatException errors caused by spaces, commas, or unexpected characters in the data. Since MapReduce frequently parses raw text files (like CSVs or flat text), fields that represent numerical values like “income” are often read as strings. If a data entry contains a blank space, a currency symbol, or a comma (e.g., ” 50000″, “$50,000″, or simply an empty field ” “), attempting to convert that string into an integer or double using methods like Integer.parseInt() or Double.parseDouble() will throw a NumberFormatException, potentially crashing the Mapper. Options B, C, and D represent infrastructure, installation, or query-related issues, which have nothing to do with the programmatic parsing of raw text fields within the code itself.