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Assignment No.1

This document contains an assignment for a Big Data Analytics course. It includes 3 questions that assess various topics related to big data and Hadoop. Question 1 asks students to explain the roles of different components in Hadoop and why HDFS is suited for large datasets. Question 2 asks students to write MapReduce pseudocode to multiply matrices and describe the shuffle and sort operations. Question 3 distinguishes between replication and sharding, discusses uses of key-value stores with a business example, and notes one weakness. The document also lists the course outcomes and Bloom's taxonomy levels addressed by each question.

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ishita ghutake
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81 views1 page

Assignment No.1

This document contains an assignment for a Big Data Analytics course. It includes 3 questions that assess various topics related to big data and Hadoop. Question 1 asks students to explain the roles of different components in Hadoop and why HDFS is suited for large datasets. Question 2 asks students to write MapReduce pseudocode to multiply matrices and describe the shuffle and sort operations. Question 3 distinguishes between replication and sharding, discusses uses of key-value stores with a business example, and notes one weakness. The document also lists the course outcomes and Bloom's taxonomy levels addressed by each question.

Uploaded by

ishita ghutake
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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RAMRAO ADIK INSTITUTE OF TECHNOLOGY, NERUL

DEPARTMENT OF COMPUTER ENGINEERING

Assignment-I
Subject: Big Data Analytics Academic Year: 2020-21
Class/Sem.: BE / VII Div.: A,B,C

Q.No. Question Marks CO BT


Explain the role of NameNode, DataNode, JobTracker and
Q.1 TaskTracker. Justify why is HDFS more suited for applications 06m CO1 BT4
having large datasets and not when there are small files?
Write Map Reduce Pseudo code to multiply two matrices. Illustrate
the procedure on the following matrices. Cleary show all the steps.

Q.2.a 07m CO2 BT5

Q.2.b Evaluate the operations of “shuffle” and “Sort” in the Map Reduce 08m CO2 BT4
framework? Justify with the help of to calculate Average
Temperature example.

Distinguish between replication and sharding? Discuss uses of key-


Q.3 value store with a business use-case and also state its weakness. 9m CO3 BT4

===================================================================
Course Outcomes (CO) Students’ will be able to:
CO1: Identify the key issues in big data management and use Hadoop framework for
resolving these issues.
CO2: Apply various tools and techniques for big data analytics like Hadoop, Map Reduce
and PySpark.
CO3: Understand the NoSQL Data Architecure Patterns and apply various tools and
techniques for NoSQL like MongoDB/Cassandra/ HBase/ Hypertable etc.
CO4: Apply fundamental enabling techniques and scalable algorithms for stream
mining and frequent-itemset mining.
CO5: Interpret business models and scientific computing paradigms, and apply software tools
for big data analytics.
CO6: Analyze the web links for relevant information retrieval and Achieve adequate
perspectives of big data analytics in various applications like recommender systems,
social media applications etc
----------------------------------------------------------------------------------------------------------------
Bloom's Taxonomy
BT1- Remember, BT2- Understand, BT3- Apply, BT4- Analyze, BT5- Evaluate, BT6- Create

Subject Incharge DQA Member

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