Learn why Big Data is challenging to manage and master the 4 Vs—Volume, Velocity, Variety, and Veracity—essential concepts for passing your Hadoop, MapReduce, Pig, and Hive certification exams.
Table of Contents
Question
Why is Big Data considered challenging to manage?
A. Because it always requires more storage space than SQL databases
B. Because it cannot be processed on distributed systems
C. Because of its characteristics like volume, variety, velocity, and veracity
D. Because it always comes only from IoT devices
Answer
C. Because of its characteristics like volume, variety, velocity, and veracity
Explanation
Big Data is notoriously challenging to manage because it is defined by the “4 Vs”: Volume (the sheer, massive scale of data generated), Velocity (the unprecedented speed at which new data is created and must be processed), Variety (the diverse formats of data, including structured, semi-structured, and unstructured types like video and text), and Veracity (the uncertain quality, accuracy, and reliability of the data). These intrinsic characteristics make traditional database systems inadequate, necessitating advanced distributed frameworks like Hadoop to store, process, and extract value from the information. Option A is incorrect because Big Data’s challenge is not merely about storage space compared to SQL; it is about how the data is structured and processed. Option B is factually false, as distributed systems are exactly how Big Data is processed. Option D is incorrect because Big Data originates from countless sources (social media, transactions, enterprise applications), not exclusively IoT devices.