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Why Is Big Data Considered Unpredictable in Hadoop Certification Exams?
Understand why Big Data is described as unpredictable in Hadoop and MapReduce ecosystems. Learn how rapidly changing data formats, sources, variety, and variability impact data processing and analysis.
Question
Why is Big Data described as unpredictable?
A. Because Hadoop cannot handle it properly
B. Because it only comes from sensors
C. Because it always arrives late
D. Because data formats and sources can change unexpectedly
Answer
D. Because data formats and sources can change unexpectedly
Explanation
Big Data is inherently described as unpredictable due to its characteristic of “Variety” and “Variability,” meaning the incoming data is constantly shifting in both its structure (formats) and its origins (sources). In real-world Hadoop pipelines, a single data feed might suddenly switch from cleanly structured relational data (like CSVs) to semi-structured JSON, or unstructured text and logs, without warning. Furthermore, new data sources are continuously added or modified, making it impossible to rely on rigid, predictable schemas.
Option A is incorrect because Hadoop was specifically designed to handle this unpredictability. Option B is false, as Big Data comes from social media, transactions, enterprise systems, and more, not just sensors. Option C is also incorrect; while latency can happen, Big Data is often characterized by high “Velocity” (arriving extremely fast), not inherently arriving late.