Big Data and Analytics (BCDS601) AKTU previous year questions 2018–2025
Every Big Data and Analytics question from 8 AKTU papers, tagged by unit, topic and marks. A few recent questions from each unit are listed below; open the page to filter by unit, topic or mark type.
Unit 1: Introduction to Big Data – AKTU PYQs
- Define Big Data. What are its key characteristics. (2 marks, 2025, Big Data Introduction)
- List any five big data platforms. (2 marks, 2025, Big Data Introduction)
- Describe its Big Data's evolution over time. Discuss the key drivers behind the rise of Big Data. (7 marks, 2025, Big Data Introduction)
- Discuss the various types of analytics used in Big Data. (7 marks, 2025, Big Data Analytics)
- What is the role of Big Data analytics in intelligent data analysis. (7 marks, 2025, Big Data Analytics)
Unit 2: Hadoop and MapReduce – AKTU PYQs
- How does Apache Hadoop help process the data? (2 marks, 2025, Apache Hadoop)
- What do you mean by MapReduce? List its main phases. (2 marks, 2025, MapReduce Framework)
- Describe how compression techniques and file formats impact the performance of big data processing systems (2 marks, 2025, Apache Hadoop)
- Discuss the detailed architecture of Map-Reduce. (7 marks, 2025, MapReduce Framework)
- Discuss in brief about the cluster specification. Describe how to setting up a Hadoop Cluster? (7 marks, 2025, Apache Hadoop)
Unit 3: HDFS and Hadoop Environment – AKTU PYQs
- Define the Hadoop Distributed File System (HDFS). How is it different from traditional file systems? (2 marks, 2025, HDFS Design and Concepts)
- What is HDFS in the context of Hadoop? (2 marks, 2025, HDFS Design and Concepts)
- What is the role of file system interfaces in big data storage environments (2 marks, 2025, HDFS Design and Concepts)
- Discuss Master Slave and Peer-Peer replication in detail. (7 marks, 2025, HDFS Design and Concepts)
- Explain the architecture and use cases of Apache Flume and Apache Sqoop in Big Data systems. How do these tools support efficient data ingestion from structured and unstructured sources (7 marks, 2025, HDFS Read Write Operations)
Unit 4: Hadoop Ecosystem YARN NoSQL Spark Scala – AKTU PYQs
- What are the main components of the Hadoop ecosystem? (2 marks, 2025, Hadoop Ecosystem and YARN)
- Explain briefly the role of YARN in the Hadoop ecosystem. (2 marks, 2025, Hadoop Ecosystem and YARN)
- What are the main components of the Hadoop ecosystem? (2 marks, 2025, Hadoop Ecosystem and YARN)
- What is the role of YARN in Hadoop? (2 marks, 2025, Hadoop Ecosystem and YARN)
- Evaluate the trade-offs between real-time and batch processing in distributed systems. How do technologies like YARN and Spark support both paradigms (7 marks, 2025, Apache Spark)
Unit 5: Hadoop Ecosystem Frameworks Pig Hive HBase – AKTU PYQs
- How does Hive differ from Pig in the Hadoop ecosystem? (2 marks, 2025, Apache Hive)
- What are the key differences between Hive and traditional SQL databases (2 marks, 2025, Apache Hive)
- Evaluate the use of Hive, Pig, and HBase for different Big Data application scenarios. When would you choose one over the others? (7 marks, 2025, Apache Hive)
- Explain the architecture of HIVE. Also explain data flow in HIVE. (7 marks, 2025, Apache Hive)
- Explain the role of Pig Latin as a high-level scripting language for processing large datasets in cloud environments. How does using Pig Latin on cloud-based Hadoop platforms simplify data transformation and analysis compared to traditional MapReduce programming? (7 marks, 2025, Apache Pig)