AKTU Big Data (BCS061) Question Paper 2025-26

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. What are the major challenges of conventional data systems?
2. What are the 5 Vs of Big Data?
3. Define Hadoop Distributed File System (HDFS).
4. What is Hadoop Streaming?
5. What is compression in Hadoop I/O?
6. Compare and Contrast No SQL Relational Databases.
7. What is the role of Zookeeper in Hadoop?

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Differentiate between analysis and reporting. How do modern analytics tools improve decision-making?
2. Discuss the architecture of Hadoop and explain how it supports distributed storage and parallel processing. Also describe the concept of scaling out in
3. Describe the process of setting up a Hadoop cluster. Explain configuration, security, and maintenance aspects of the Hadoop environment.
4. Explain the YARN architecture and its components. How does it improve Hadoop performance?
5. Compare Apache Pig with traditional database systems. Explain the advantages and limitations of Pig in Big Data processing. Apache Pig Big Data

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the challenges of Big Data such as storage, scalability, and data quality. Suggest suitable solutions using modern technologies.
2. A global e-commerce platform generates high-volume transactional data, real-time clickstreams, and social media feedback. Analyze this scenario using the 5 Vs of Big Data and propose a complete Big Data architecture. Justify your choice of tools and technologies.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Differentiate between ‘scale-up’ and ‘scale-out’ approaches in computing systems. Explain in detail how Hadoop uses the scale-out approach to improve system performance, reliability, and fault tolerance, with suitable examples and diagrams.
2. Describe how a MapReduce job is executed in Hadoop. Explain the roles of job scheduling, task execution, and data flow during processing.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain how data is written to HDFS. Describe each step involved in storing a file, including block creation and replication.
2. Explain the role of Flume and Sqoop in Hadoop. Describe how these tools are used for data ingestion from different sources into HDFS. Hadoop Flume Sqoop

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe the working of MongoDB. Explain how documents are stored, queried, and indexed in MongoDB.
2. Explain the architecture of Apache Spark. Describe the concepts of Resilient Distributed Databases (RDDs), jobs, stages, and tasks in data processing. Apache Spark Resilient

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Compare HBase with relational databases. Explain the advantages of HBase in handling large-scale and real-time data.
2. A large dataset needs to be processed and analyzed using Hive or Pig. Explain how you would perform data loading, querying, and analysis, and describe how execution happens internally.

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