About this previous year paper
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
| Hive | Pig |
|---|---|
| SQL-like (HiveQL) | Script-based (Pig Latin) |
| Used for querying | Used for data transformation |
| Declarative | Procedural |
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
| Analysis | Reporting |
|---|---|
| Insight-oriented | Summary-oriented |
| Predictive | Historical |
| Supports decisions | Shows results |