🎯 MSU MCA Sem II — April 2026 Big Data Analytics

Most Important Questions (Based on Syllabus + Previous Paper Analysis)

This guide is specially prepared for students of Manonmaniam Sundaranar University (MSU) MCA Semester II. It includes the most expected questions for the Big Data Analytics exam based on syllabus coverage and previous year question paper analysis.

Keywords: MSU MCA Big Data Analytics important questions, Hadoop exam questions, MapReduce, MongoDB, Pig, Hive notes

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📊 What is Big Data Analytics?

Big Data Analytics refers to the process of examining large and complex datasets to uncover hidden patterns, correlations, and insights. It uses technologies like Hadoop, MapReduce, MongoDB, Hive, and Pig to process massive data efficiently.

• Volume: Large amount of data
• Velocity: Speed of data generation
• Variety: Different data types
• Veracity: Data reliability
• Value: Meaningful insights

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📝 PART A — MCQ (15 × 1 = 15 marks)

Must-know topics:

Topic	Expected MCQ
Structured vs Unstructured data	Example identification
Big Data characteristics	3V's / 5V's
Hadoop	Definition, best description
NoSQL	Characteristics vs SQL
MapReduce	Mapper function role
MongoDB	Document = Row, Collection = Table
Hive	Supported data types, JOIN types
Pig	Architecture, execution method
Data Scientist	Primary responsibility
Distributed computing	Main challenges

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📝 PART B — Short Answer (5 × 4 = 20 marks)

Choose either (a) or (b) — ≤250 words

Unit I

• (a) Evolution, characteristics & challenges of Big Data
• (b) Big Data Analytics vs Traditional Business Intelligence

Unit II

• (a) MapReduce programming model with example
• (b) NameNode & DataNode roles in HDFS

Unit III

• (a) Indexing concept in MongoDB
• (b) Data compression in MapReduce — importance & types

Unit IV

• (a) Primitive & complex data types in Pig
• (b) UDFs in Pig — purpose, creation & usage

Unit V (Pig)

• (a) Relational operators in Pig
• (b) Piggybank — purpose & description

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📝 PART C — Essay (5 × 8 = 40 marks)

Choose either (a) or (b) — ≤600 words

Unit I

• (a) Types of analytics in Big Data environments + top tools
• (b) Data Science — definition & responsibilities in Big Data

Unit II

• (a) Hadoop ecosystem — core components, roles & challenges
• (b) Apache Pig & Apache Hive — role in simplifying data processing

Unit III

• (a) MongoDB key terms vs RDBMS comparison
• (b) Indexing in MongoDB — importance & types

Unit IV

• (a) Hive serialization & deserialization
• (b) Aggregation functions in Hive — GROUP BY, HAVING

Unit V

• (a) Apache Pig — purpose, architecture & role in Big Data
• (b) Pig primitive data types — list & explain

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📘 Sample Model Answer (Important)

MapReduce Programming Model (Short Answer):

MapReduce is a programming model used for processing large datasets in distributed systems like Hadoop. It consists of two main phases:

• Map Phase: Processes input data and converts it into key-value pairs
• Reduce Phase: Aggregates and summarizes the output

Example: Word Count — Mapper counts words, Reducer sums them.

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⭐ Top 5 "Sure Shot" Topics

1. Hadoop Ecosystem — components & HDFS
2. MapReduce — Mapper, Reducer with Word Count example
3. MongoDB vs RDBMS — terms comparison
4. Apache Pig — architecture, Pig Latin, relational operators
5. Big Data characteristics — 5V's + analytics types

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💡 Exam Preparation Tips

• Focus on diagrams and definitions
• Practice previous year questions
• Write answers with keywords
• Revise important topics multiple times

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⚠️ Disclaimer

This content is created for educational and exam preparation purposes only for students of Manonmaniam Sundaranar University.

• Questions are based on syllabus and previous year analysis
• This is not an official question paper
• Final exam questions may vary

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