AKTU Big Data and Analytics (BCDS601) 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. Briefly explain 5Vs of Big Data.
2. What is the significance of the 'Heartbeat' signal in a Hadoop Cluster?
3. What is the purpose of 'Capped Collections' in MongoDB?
4. List the basic data types supported by the Scala programming language.
5. Define 'Resilient Distributed Datasets (RDD)' in Spark.
6. What is the function of the 'Hive Metastore'?
7. Mention the use of 'Zookeeper' in cluster monitoring.

Section B

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

1. Discuss Big Data Privacy and Ethics. What are the major challenges in auditing big data?
2. Discuss the architecture of HDFS. Explain how it handles file abstraction and replication.
3. Differentiate between NoSQL and RDBMS. Discuss the key characteristics of NoSQL databases.
4. Describe the YARN Architecture. How does it support running MRv1 applications?
5. Explain Apache Pig’s Data Model. Why is Pig Latin preferred for data transformation?

Section C

Q3

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

1. Explain the Big Data Technology Stack. Discuss the challenges of conventional systems.
2. Compare and contrast Analysis vs. Reporting. Discuss modern data analytic tools.

Q4

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

1. Describe the process of Data Ingestion using Flume and Sqoop.
2. Explain the HDFS Read and Write paths. How does the client interact with the NameNode?

Q5

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

1. Explain the querying mechanism in MongoDB. How does it handle complex data structures compared to traditional SQL queries?
2. Compare and contrast the Schema-less nature of NoSQL databases with the Schema-oriented nature of traditional RDBMS. Discuss the advantages of using NoSQL for Big Data.

Q6

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

1. Analyze the Resilient Distributed Dataset (RDD) model in Spark. How does Spark's "In-Memory" processing provide a performance advantage over the traditional MapReduce disk-based approach?
2. Discuss the importance of closures and higher-order functions in Scala. How do these features simplify the development of distributed processing logic for Spark?

Q7

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

1. Compare Local Mode and MapReduce Mode in Apache Pig. In what specific scenarios would a developer choose the Grunt shell over a pre- written Pig script?
2. Analyze why HBase is superior for random, real-time read/write access to Big Data compared to HDFS alone or a traditional RDBMS. Discuss its Schema Design and Advanced Indexing capabilities.

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