Big Data Analytics model question paper (CCS334)

Big Data Analytics (CCS334) model question paper 

#### Part A 


1. Define Big Data and give an example of an industry application.
2. What is unstructured data and why is it important in Big Data analytics?
3. Explain the key characteristics of NoSQL databases.
4. What are graph databases and where are they commonly used?
5. Describe the purpose of Hadoop Distributed File System (HDFS).
6. What is Hadoop Streaming and how does it differ from Hadoop Pipes?
7. Explain the concept of MapReduce.
8. What is the role of YARN in Hadoop?
9. Describe the data model of HBase.
10. What is Pig Latin and what is its purpose in Big Data processing?

 Part B (65 marks)


1. a) Discuss the convergence of key trends that have led to the rise of Big Data. Provide industry examples to illustrate your points. (13 marks)
   
   b) Explain the role of cloud technologies and mobile business intelligence in the Big Data ecosystem. (13 marks)

2. a) Describe the key-value and document data models in NoSQL databases. Provide examples to illustrate their use. (13 marks)
   
   b) Discuss the Cassandra data model and provide examples of how Cassandra clients interact with the database. (13 marks)

3. a) Explain the design and key concepts of the Hadoop Distributed File System (HDFS). (13 marks)
   
   b) Describe the process of analyzing data with Hadoop and the role of Avro in data serialization. (13 marks)

4. a) Discuss the anatomy of a MapReduce job run and explain how job scheduling and task execution work in Hadoop. (13 marks)
   
   b) Explain the concept of MapReduce types and the different input and output formats used in MapReduce. (13 marks)


5. a) Describe the data model and implementations of HBase. Provide examples of how HBase clients interact with the database. (13 marks)
  
   b) Explain the components of Hive and discuss how HiveQL is used for data definition, manipulation, and querying. (13 marks)

#### Part C (15 mark)


1. Explain the integration of Cassandra with Hadoop and discuss how it enhances data processing capabilities. (15 marks)
   
2. Discuss the MapReduce workflow and explain the use of MRUnit for unit testing MapReduce jobs. Provide examples to illustrate your points. (15 marks)

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