RTU Kota B.Tech 7th Semester Big Data Analytics Question Paper 2023 (IT)

RTU Big Data Analytics 2023 Paper Review

The Big Data Analytics course for Information Technology students at Rajasthan Technical University focuses on the architectural shifts required to process massive datasets. The 2023 examination emphasized the move from vertical scaling (increasing hardware power) to horizontal scaling (distributing data across commodity hardware). For IT students, this subject is foundational for understanding how modern web platforms manage logs, user behavior data, and real-time streams.

The 2023 paper demanded a clear understanding of the internal mechanics of Hadoop and the mathematical logic behind parallel processing. This review breaks down the exam structure to help you prepare for similar assessments.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks, organized into three parts:

• Part A (20 Marks): Ten compulsory questions, two marks each. Expect definitions of Big Data, the "5 Vs," HDFS architecture components (NameNode, DataNode, Secondary NameNode), and basic NoSQL concepts. Keep answers concise—under 25 words.
• Part B (20 Marks): Seven questions; answer five. Each is worth four marks. These are analytical. Prepare to explain the benefits of distributed file systems, the role of Zookeeper, the difference between SQL and NoSQL, and the lifecycle of a MapReduce job.
• Part C (30 Marks): Five major questions; answer three. Each is worth ten marks. These require detailed technical explanations. Prepare for long-form questions on HDFS write/read operations, the internal workings of the MapReduce framework, the CAP theorem, and data partitioning techniques.

Core Topics Evaluated in the 2023 Curriculum

Focus your study time on these specific modules to maximize your score:

Hadoop Distributed File System (HDFS)

Master the architecture. You must be able to draw and explain the communication flow between the client, NameNode, and DataNode during both read and write operations. Understand how HDFS handles replication and fault tolerance.

The MapReduce Programming Model

This is the core of batch processing. Learn the lifecycle: Map, Shuffle, Sort, and Reduce. You should be able to provide a step-by-step trace of how a simple application, such as "Word Count," processes a large input file. Understand how the framework manages task scheduling and data locality.

NoSQL Databases

Understand why relational databases fail at Big Data scale. Study the four main types of NoSQL databases:

• Key-Value Stores: Simple and fast.
• Document Stores: Flexible schemas (e.g., MongoDB).
• Column-Family Stores: Optimized for read-heavy queries (e.g., HBase).
• Graph Databases: Optimized for relational complexity.

Distributed System Theory

Understand the CAP Theorem (Consistency, Availability, and Partition Tolerance). You must be able to explain why a distributed system can only guarantee two out of the three properties at any given time.

Answer Writing Strategy for High Marks

RTU evaluators look for logical progression and clear, structured technical content:

• Diagrams: Use a ruler and black pen for architectural diagrams. A clean sketch of an HDFS block structure or a MapReduce data flow is essential for securing top marks in Part C.
• Formatting: Use a black pen for algorithm names and formulas. Use a blue pen for explanatory text. Use bullet points for features, advantages, and limitations to make your answers scannable.
• Precision: If the question asks for a framework component (like a NameNode), define its exact responsibility. When explaining algorithms, provide a clear, step-by-step procedure.
• Comparative Tables: Whenever the paper asks to compare two concepts—like "HDFS vs. Standard File Systems" or "MapReduce vs. Spark"—always use a table to clearly delineate their differences.

Time Management During the Exam

• Part A (20 minutes): Answer these first. Aim for short, technical definitions that hit the key concepts.
• Part B (40 minutes): Limit each answer to 8 minutes. Focus on drawing the required diagrams early.
• Part C (120 minutes): Dedicate 40 minutes per major question. Use this time to write out full architectural explanations and detailed algorithmic traces. If a question involves data flow, define it, state the steps, provide a small example, and mention the real-world utility.