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Why Is Parallel Processing Essential for Managing Large-Scale Datasets in Hadoop?
Master the core concepts of Hadoop for your Big Data certification. Learn how distributed storage and parallel processing across clusters enable efficient management of massive datasets unlike traditional single-server solutions.
Question
Why is Hadoop considered a solution for Big Data?
A. It converts unstructured data into SQL queries automatically
B. It eliminates the need for servers in data processing
C. It reduces the size of datasets
D. It enables distributed storage and parallel processing across clusters
Answer
D. It enables distributed storage and parallel processing across clusters
Explanation
Hadoop serves as a fundamental solution for Big Data because it moves away from centralized processing to a distributed architecture capable of handling petabytes of information. It utilizes the Hadoop Distributed File System (HDFS) to split massive datasets into blocks that are stored redundantly across multiple nodes in a cluster, rather than on a single server. Simultaneously, it employs processing models (originally MapReduce) that bring the computation to the data, allowing valid parallel processing on commodity hardware to achieve high throughput and linear scalability.