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BIG DATA AND ANALYTICS (BCDS601)
B.Tech Semester VI – Complete Exam Preparation Notes
SECTION A
(Attempt all | 2 × 7 = 14 marks)
Write 2–3 crisp lines for each answer.
(a) Define Big Data. What are its key characteristics?
Big Data refers to large, complex datasets that cannot be efficiently processed using traditional data processing tools.
Key characteristics are 5 Vs: Volume, Velocity, Variety, Veracity, and Value.
(b) Hadoop Distributed File System (HDFS) & Difference from Traditional File Systems
HDFS is a distributed storage system designed to store very large files across multiple machines.
Unlike traditional file systems, HDFS provides fault tolerance, scalability, and high throughput rather than low latency.
(c) What is MapReduce? List its main phases
MapReduce is a programming model for processing large datasets in parallel.
Main phases: Map → Shuffle & Sort → Reduce.
(d) Impact of Compression Techniques & File Formats on Performance
Compression reduces storage space and I/O cost, improving performance.
Efficient file formats like Avro, Parquet, and ORC support faster data access and better query optimization.
(e) Role of File System Interfaces in Big Data Storage
File system interfaces provide standard access methods to store, retrieve, and manage data across distributed environments, ensuring compatibility and scalability.
(f) Role of YARN in Hadoop Ecosystem
YARN (Yet Another Resource Negotiator) manages cluster resources and job scheduling, enabling multiple data processing engines like MapReduce and Spark to run on Hadoop.
(g) Differences between Hive and Traditional SQL Databases
| Hive | Traditional SQL |
|---|---|
| Built on Hadoop | Runs on RDBMS |
| Batch processing | Real-time processing |
| Uses HiveQL | Uses SQL |
| High latency | Low latency |
SECTION B
(Attempt any THREE | 7 × 3 = 21 marks)
(a) Evolution of Big Data & Key Drivers
Big Data evolved from simple databases → data warehouses → distributed systems.
Key drivers include social media growth, IoT devices, cloud computing, and cheap storage.
(b) Role of Big Data Analytics in Intelligent Data Analysis
Big Data analytics helps extract patterns, trends, and insights from large datasets, enabling predictive analysis, automation, and better decision-making.
(c) Apache Flume & Apache Sqoop: Architecture and Use Cases
Flume collects unstructured data (logs, events) into Hadoop.
Sqoop transfers structured data between RDBMS and Hadoop.
Both enable efficient data ingestion from diverse sources.
(d) Real-Time vs Batch Processing & Role of YARN and Spark
Batch processing handles large historical data, while real-time processing handles live data streams.
YARN manages resources, while Spark supports both batch and real-time (streaming) processing.
(e) Hive vs Pig vs HBase: Use-Case Evaluation
| Tool | Best Use Case |
|---|---|
| Hive | Data warehousing & analytics |
| Pig | Data transformation & scripting |
| HBase | Real-time read/write access |
Choice depends on latency, data structure, and application needs.
SECTION C
(Attempt any ONE | 7 marks)
(a) Importance & Applications of Big Data in Industries
Healthcare: disease prediction, patient analytics
Finance: fraud detection, risk analysis
E-commerce: recommendation systems, customer behavior analysis
Big Data improves accuracy, speed, and data-driven decisions.
OR
(b) Big Data Privacy & Ethical Concerns
Major concerns include data misuse, lack of consent, security breaches, and bias.
Auditing and compliance ensure legal adherence, transparency, and accountability.