Papers / AKTU / B.Tech / Big Data and Analytics
AKTU · B.Tech · 6th semester

AKTU Big Data and Analytics (KDS601) Question Paper 2023-24

UniversityDr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow
CourseB.Tech, Semester VI
SubjectBig Data and Analytics (KDS601)
ExamTheory Examination 2023-24 (even semester, held June 2024)
Time · Marks3 hours · 100 marks
SourceAKTU question paper, via Notes Gallery
View original PDF See important topics

Note: Attempt all sections. If you require any missing data, then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

  1. List the differences between structured, semi-structured, and unstructured data. [2]
  2. Write short note on Drivers of Big Data. [2]
  3. List the core functionalities of Apache Hadoop. [2]
  4. Explain how the data format of Hadoop is important. [2]
  5. List the steps involved in using Sqoop to import data from RDBMS into Hadoop. [2]
  6. Define how file system Works. [2]
  7. List the key differences between the fair scheduler and the capacity scheduler in Hadoop. [2]
  8. List the Data Type used in Mango DB. [2]
  9. Define HiveQL and its key features. [2]
  10. List out the Data Processing Operators used in Pig. [2]

Section B

Q2. Attempt any three · 10 × 3 = 30 marks

  1. Discuss why Big Data is crucial for modern businesses and industries. [10]
  2. Discuss Hadoop Distributed File System (HDFS), and how does it work. [10]
  3. Discuss the benefits and challenges of using HDFS for big data storage and processing. [10]
  4. Differentiate NoSQL databases from traditional RDBMS. Also list the key characteristics and use cases for NoSQL databases. [10]
  5. Discuss the role of Zookeeper in monitoring a Hadoop cluster. [10]

Section C

Q3 to Q7. Attempt any one part of each question · 10 × 5 = 50 marks

Q3

  1. Distinguishes Big Data analytics from traditional data analytics. Also list the techniques and tools used in Big Data analytics. [10]
  2. Discuss the various Big Data Features in terms of Security, Protection and Auditing. [10]

Q4

  1. Discuss the roles of additional components like HBase, Pig, and Hive in the Hadoop ecosystem. [10]
  2. Discuss the Working of Map Reduce and its Characteristics. [10]

Q5

  1. Explain how HDFS implement data replication and list its advantages. [10]
  2. Discuss the Security issues in Hadoop and why it is important for Data analysis. [10]

Q6

  1. Explain how documents can be created, updated, and deleted in MongoDB. [10]
  2. Explain the Various Ecosystem Components used in Hadoop with proper example. [10]

Q7

  1. Explain the IBM's overall strategy for Big Data, and its key components. [10]
  2. Discuss Big SQL, and how it extends SQL capabilities to Big Data environments. [10]

Most repeated topics

How many of the last 3 AKTU 6th-semester Big Data and Analytics papers (KDS-601 2022-23, KDS601 2023-24, BCDS601 2024-25) asked about each topic.

HDFS (working, benefits, replication)
3 / 3
MapReduce working and phases
3 / 3
Hadoop ecosystem: Hive, Pig, HBase
3 / 3
NoSQL databases and MongoDB
3 / 3
Big Data security, privacy and auditing
3 / 3
Big Data drivers and characteristics
3 / 3
Job scheduling and YARN
3 / 3
Data ingestion with Sqoop and Flume
2 / 3
Zookeeper
2 / 3
IBM Big Data strategy, InfoSphere, Big SQL
2 / 3
Spark vs MapReduce
1 / 3

Counted by LearnAIFast from the 2022-23, 2023-24 and 2024-25 even-semester theory papers.

Answer hints

Q1(g). Fair scheduler vs capacity scheduler

The fair scheduler shares cluster resources so every running job gets an equal share over time. The capacity scheduler divides the cluster into queues, each guaranteed a set percentage, which suits several teams sharing one cluster. Spare capacity in one queue can be lent to others.

Q2(d). NoSQL vs RDBMS

RDBMS: fixed schema, tables and joins, ACID transactions, scales up on one bigger server. NoSQL: flexible schema, scales out across many servers, often trades strict consistency for availability (BASE). Types: document (MongoDB), key–value (Redis), column-family (HBase, Cassandra), graph (Neo4j). Use cases: real-time web apps, IoT data, catalogues, social feeds.

Q5(a). HDFS data replication

Each block is stored on several DataNodes, 3 by default. With rack awareness, one copy goes on the local rack and two on another rack, so a whole rack can fail without data loss. The NameNode tracks replicas through heartbeats and re-replicates blocks when a node dies. Advantages: fault tolerance, high availability and faster parallel reads.

Q6(a). MongoDB create, update, delete

Create: db.students.insertOne({name: "Asha", marks: 82}). Update: db.students.updateOne({name: "Asha"}, {$set: {marks: 90}}). Delete: db.students.deleteOne({name: "Asha"}). Mention insertMany, updateMany and deleteMany for multiple documents, and that documents are stored as BSON.

Other papers

Big Data and Analytics 2022-23 (full page) Big Data and Analytics 2024-25 · PDF Data Analytics 2025-26 All AI & ML papers

This paper is shared for study purposes only. Copyright belongs to AKTU. If you own the copyright and want it removed, email learnwithskillnest@gmail.com and we will take it down.