Big Data often involves a form of distributed storage and processing using Hadoop and MapReduce.

One reason for this is:

A) the processing power needed for the centralized model would overload a single computer.

B) Big Data systems have to match the geographical spread of social media.

C) centralized storage creates too many vulnerabilities.

D) the "Big" in Big Data necessitates over 10,000 processing nodes.

Question

Jaqueline Macdonald

Computers & Technology

1 December, 08:26

Big Data often involves a form of distributed storage and processing using Hadoop and MapReduce.

One reason for this is:

A) the processing power needed for the centralized model would overload a single computer.

B) Big Data systems have to match the geographical spread of social media.

C) centralized storage creates too many vulnerabilities.

D) the "Big" in Big Data necessitates over 10,000 processing nodes.

+5

Answers (1)

Know the Answer?

Mckenna Craig · Answer 1

One reason for this is: the processing power needed for the centralized model would overload a single computer.

Explanation:

Companies would be engrossed in achieving and examining the datasets because they can supplement significant value to the desicion making method. Such processing may include complicated workloads. Furthermore the difficulty is not simply to save and maintain the massive data, but also to investigate and extract a essential utilities from it.

Processing of Bigdata can consists of various operations depending on usage like culling, classification, indexing, highlighting, searching etc. MapReduce is a programming model used to process large dataset workloads. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage.