Big Data and Analytics PYQ 2017-18 AKTU Question Paper

AKTU · BTECH · Semester 6 · Big Data and Analytics · Session 2017-18 · PYQ

AKTU Big Data and Analytics previous year question paper 2017-18 for B.Tech Semester 6. Covers Introduction to Big Data, Hadoop and MapReduce, HDFS and…

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Big Data and Analytics AKTU syllabus

  1. Unit 1: Introduction to Big Data
  2. Unit 2: Hadoop and MapReduce
  3. Unit 3: HDFS and Hadoop Environment
  4. Unit 4: Hadoop Ecosystem YARN NoSQL Spark Scala
  5. Unit 5: Hadoop Ecosystem Frameworks Pig Hive HBase

Questions in Big Data and Analytics AKTU PYQ 2017-18

  1. Q1a. What is big data, why we need to analyze big data? (2 marks, 2017-18)
  2. Q1b. Define "Data Locality Optimization". (2 marks, 2017-18)
  3. Q1c. List down the tools related with Hadoop. (2 marks, 2017-18)
  4. Q1d. State the purpose of Hadoop Pipes. (2 marks, 2017-18)
  5. Q1e. What is map reducing? (2 marks, 2017-18)
  6. Q1f. Write the difference between operational and analytical system. (2 marks, 2017-18)
  7. Q1g. Explain Hadoop distributed file system. (2 marks, 2017-18)
  8. Q1h. Write down any four industry examples for Big Data. (2 marks, 2017-18)
  9. Q1i. List down the entity of YARN. (2 marks, 2017-18)
  10. Q1j. What is Hadoop architecture? (2 marks, 2017-18)
  11. Q2a. Why crowd sourcing analytics needed? Explain. (10 marks, 2017-18)
  12. Q2b. Illustrate on how cloud and big data related to each other. (10 marks, 2017-18)
  13. Q2c. Discuss the design of Hadoop Distributed File System (HDFS) in detail. (10 marks, 2017-18)
  14. Q2d. Discuss the queries involved in Hive data definition. (10 marks, 2017-18)
  15. Q2e. Write in detail about Hbase data model and pig data model. (10 marks, 2017-18)
  16. Q3a. How does Hadoop system analyze data? Explain your answer with example. (10 marks, 2017-18)
  17. Q3b. Explain Cassandra data model. (10 marks, 2017-18)
  18. Q4a. Explain the Anatomy of MapReduce job run. (10 marks, 2017-18)
  19. Q4b. Discuss the different types and formats of Map Reduce with examples. (10 marks, 2017-18)
  20. Q5a. With the help of a Data Model explain aggregations and relations. (10 marks, 2017-18)
  21. Q5b. Write a brief note on composing map-reduce calculation. (10 marks, 2017-18)
  22. Q6a. Explain Master slave and peer-peer replication in detail. (10 marks, 2017-18)
  23. Q6b. Discuss about the three dimensions of Big Data. (10 marks, 2017-18)
  24. Q7a. Describe about graph database and schema less databases. (10 marks, 2017-18)
  25. Q7b. Elaborate on graph mapping schemas. What do you mean by lower bounds replication rate? (10 marks, 2017-18)

AKTU paper codes: BCDS601, BCS061, KDS601, KCS061, NIT067

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