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Identify fundamental Big Data Analytics frameworks and logically characterize them.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Learning Outcomes:

LO1. Demonstrate the understanding of basic concepts of Big Data, its importance and need in business context.

LO2. Explain the various components of Hadoop and HFDC along with their role in the Big Data ecosystem.

LO3. Summarize the learning on Big Data analytics using Yarn, HDFC and MapReduce.

Assessment Criteria: Weighting 100%

3000 words

Tasks (All tasks are equally weighted):

TASK 1 (this task is related to LO1). Describe and characterise the concept of 'big data'. Also, describe and explain what we mean by 'big data analytics'. Moreover, you should make a logical and technical comparison between 'data analytics' and 'big data analytics'.

TAKS 2 (this task is related to LO1 and LO2). Identify fundamental Big Data Analytics frameworks and logically characterize them.

TASK 3 (this task is related to LO2 and LO3). Try to fundamentally describe Distributed Processing frameworks and explain why (and how) they can support big data analytics.

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