Why Is Hadoop the Best Solution for Processing Big Data?
Learn why Hadoop is perfectly suited for processing Big Data for your certification exam. Understand how distributing storage and computation across multiple nodes enables scalable, fault-tolerant analysis of massive datasets.
Question
Why is Hadoop suited for processing Big Data?
A. It distributes storage and computation across multiple nodes
B. It only processes structured relational data
C. It avoids using replication to save space
D. It compresses all data into a single block
Answer
A. It distributes storage and computation across multiple nodes
Explanation
Hadoop is specifically suited for processing Big Data because of its distributed architecture. Instead of relying on a single, expensive supercomputer, Hadoop splits massive datasets and distributes both the storage (via HDFS) and the parallel processing computation (via MapReduce or YARN) across a cluster of many inexpensive commodity servers. This approach allows for horizontal scalability, high throughput, and fault tolerance. The other options are incorrect: Hadoop processes all data types (structured, semi-structured, and unstructured), heavily relies on replication (usually 3 copies) for fault tolerance, and breaks data into many distributed blocks rather than compressing it into one.