(SEM VI) THEORY EXAMINATION 2023-24 BIG DATA AND ANALYTICS

KDS601 – BIG DATA AND ANALYTICS (B.Tech Sem VI, 2023–24)

All answers are written in simple, clear, humanized language (not short bullet points) and are prepared strictly according to the uploaded question paper (Page 1).
Reference: Uploaded Question Paper 

KDS601-BIG-DATA-AND-ANALYTICS

SECTION A

Attempt all questions in brief (2 × 10 = 20 marks)

(a) Differences between structured, semi-structured, and unstructured data

Structured data is highly organized and stored in rows and columns, such as data in relational databases. Semi-structured data does not follow a fixed schema but uses tags or markers, for example JSON and XML files. Unstructured data has no predefined format and includes text documents, images, videos, emails, and social media content.

(b) Drivers of Big Data

Big Data is driven by the rapid growth of social media, mobile devices, IoT sensors, cloud computing, digital transactions, and online services. These sources continuously generate large volumes of diverse and fast-moving data.

(c) Core functionalities of Apache Hadoop

Apache Hadoop provides distributed data storage using HDFS, parallel data processing using MapReduce, resource management using YARN, and fault tolerance through data replication across multiple nodes.

(d) Importance of Hadoop data format

Hadoop data formats such as Avro, Parquet, and ORC affect storage efficiency, compression, and processing speed. Optimized formats reduce disk usage and improve query performance, making large-scale data analysis faster and more efficient.

(e) Steps to import data from RDBMS to Hadoop using Sqoop

First, Sqoop establishes a connection with the RDBMS. Then, it analyzes table metadata, divides data into splits, launches parallel Map tasks, and finally imports the data into HDFS or Hive tables.

(f) How a file system works

A file system manages how data is stored, organized, and retrieved. It maintains file names, directories, permissions, and metadata, and ensures efficient access and storage of data on physical disks.

(g) Fair Scheduler vs Capacity Scheduler in Hadoop

The Fair Scheduler allocates resources equally among running jobs, ensuring fairness. The Capacity Scheduler divides resources into queues with fixed capacities, ensuring guaranteed resource availability for different organizations.

(h) Data types used in MongoDB

MongoDB supports data types such as String, Integer, Boolean, Double, Date, Array, Object, Null, and ObjectId, allowing flexible schema design.

(i) HiveQL and its key features

HiveQL is a SQL-like query language used in Apache Hive. It supports table creation, querying, partitioning, and integration with Hadoop, allowing users to analyze big data without writing complex MapReduce code.

(j) Data processing operators in Pig

Pig supports operators such as LOAD, FILTER, GROUP, JOIN, FOREACH, ORDER, DISTINCT, and STORE to perform data transformation and analysis.

SECTION B

Attempt any three (10 × 3 = 30 marks)

(a) Why Big Data is crucial for modern businesses and industries

Big Data helps organizations analyze customer behavior, optimize operations, improve decision-making, and gain competitive advantage. Industries use Big Data for fraud detection, predictive maintenance, personalized marketing, risk analysis, and innovation. Data-driven insights enable faster and more accurate business strategies.

(b) Hadoop Distributed File System (HDFS) and its working

HDFS is a distributed storage system designed for large datasets. Files are divided into blocks and stored across multiple DataNodes. The NameNode manages metadata, while DataNodes store actual data. Replication ensures fault tolerance and reliability even if a node fails.

(c) Benefits and challenges of using HDFS

HDFS provides scalability, fault tolerance, and cost-effective storage. It supports parallel processing of big data. However, it is not suitable for small files, real-time access, or low-latency applications and requires skilled administration.

(d) NoSQL databases vs traditional RDBMS

Traditional RDBMS use fixed schemas, SQL queries, and vertical scaling. NoSQL databases offer flexible schemas, horizontal scaling, and high availability. NoSQL systems are ideal for big data applications like social media, IoT, and real-time analytics.

(e) Role of ZooKeeper in Hadoop cluster monitoring

ZooKeeper coordinates distributed applications by providing configuration management, synchronization, leader election, and fault detection. It ensures high availability and consistency across Hadoop clusters.

SECTION C

3(a) Big Data analytics vs traditional data analytics

Traditional analytics deals with structured, small-scale data using centralized systems. Big Data analytics handles massive, diverse, and fast-moving data using distributed systems. Tools like Hadoop, Spark, Hive, and Pig are used in Big Data analytics, while traditional analytics relies on RDBMS and data warehouses.

3(b) Big Data features: Security, Protection, and Auditing

Big Data security includes authentication, authorization, encryption, and access control. Data protection ensures confidentiality and integrity, while auditing tracks user actions and data access to ensure compliance and accountability.