(SEM VI) THEORY EXAMINATION 2024-25 BIG DATA

BCS061 – BIG DATA

Time: 3 Hours | Max Marks: 70

SECTION A – Short Answer Questions

(2 × 7 = 14 marks | Attempt ALL)

Write definition + 1–2 key points

1. List Any Five Big Data Platforms

Apache Hadoop                               Apache Spark

Apache Flink                                    Apache Storm

Google BigQuery

2. How Does Apache Hadoop Help Process Data?

Apache Hadoop enables distributed storage and parallel processing of large datasets using HDFS and MapReduce, making big data processing scalable and fault-tolerant.

3. What is HDFS?

HDFS (Hadoop Distributed File System) stores large files across multiple nodes with replication for fault tolerance and high availability.

4. Difference Between Hive and Pig

5. Main Components of Hadoop Ecosystem

HDFS (Storage)                                             MapReduce (Processing)

YARN (Resource management)                    Hive, Pig, HBase, Spark (Tools)

6. Role of YARN in Hadoop

YARN manages cluster resources, schedules jobs, and allows multiple processing engines to run on the same Hadoop cluster.

7. Role of YARN in Hadoop (Repeated in paper)

YARN separates resource management from job scheduling, improving scalability and efficiency.

SECTION B – Medium Answer Questions

(7 × 3 = 21 marks | Attempt ANY THREE)
Write concept → explanation → diagram/example

1. Types of Analytics in Big Data

Descriptive: What happened?                                          Diagnostic: Why did it happen?

Predictive: What will happen?                                          Prescriptive: What should be done?

Used in business intelligence and decision making.

2. Master–Slave vs Peer-to-Peer Replication

Master–Slave:                                                                   One master node controls slaves

Easier management                                                            Single point of failure

Peer-to-Peer:                                                                   All nodes equal

High fault tolerance                                                          Complex synchronization

3. Architecture of MapReduce

Input Split                                                                       Mapper → key-value pairs

Shuffle & Sort                                                                Reducer → final output

Used for parallel data processing.

4. Hadoop Cluster Specification & Setup                         Hardware: nodes, CPU, RAM, storage

Software: Java, Hadoop                                                    Configuration: core-site.xml, hdfs-site.xml

Start NameNode, DataNode, ResourceManager

5. Architecture & Data Flow in HIVE                                 User submits query

Driver receives query                                                        Compiler converts to MapReduce

Execution engine runs job                                                 Results stored in HDFS

(Hive sits on top of Hadoop)

SECTION C – Attempt ANY ONE

(7 marks)

1. 5 Vs of Big Data (Very Important)

Volume: Huge data size                                                     Velocity: Speed of data generation

Variety: Structured, semi-structured, unstructured           Veracity: Data quality

Value: Useful insights                                                        Example: Social media data.

2. Analysis vs Reporting in Big Data