Big Data

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 Big Data

Module 1: Introduction to Big Data (8 Questions)

  1. Define Big Data. What are its key characteristics (5 Vs)?

  2. Explain the challenges of Big Data management.

  3. Describe the architecture of a Big Data platform.

  4. What is data analytics? Explain its importance in today’s world.

  5. Explain the difference between traditional data and Big Data.

  6. Discuss intelligent data analysis with suitable examples.

  7. What are analytical processes and tools used in Big Data?

  8. Explain the role of Big Data in business decision-making.



Module 2: Mining Data Streams (9 Questions)

  1. What is a data stream? How is it different from static data?

  2. Explain the stream data model and architecture.

  3. Discuss the challenges involved in stream processing.

  4. What is sampling in a stream? Explain its importance.

  5. Explain filtering and counting distinct elements in a data stream.

  6. What is a sliding window model? Give examples.

  7. Discuss the concept of decaying window and its use.

  8. What is a real-time analytics platform (RTAP)? Give examples of applications.

  9. Write short notes on real-time sentiment analysis and stock market prediction using data streams.



Module 3: Hadoop (9 Questions)

  1. Explain the architecture of the Hadoop Distributed File System (HDFS).

  2. What are the main components of Hadoop?

  3. Explain how Hadoop scales for large datasets.

  4. Discuss how Hadoop Streaming works.

  5. Explain the concept of data replication in HDFS.

  6. Describe the steps involved in developing a MapReduce application.

  7. Explain the working of Map and Reduce functions with examples.

  8. What are Hadoop’s scheduling algorithms like FIFO and Fair Scheduling?

  9. Discuss various Hadoop file formats and MapReduce types.



Module 4: Frameworks (9 Questions)

  1. What are Big Data frameworks? Why are they required?

  2. Explain how Pig simplifies data processing.

  3. Write short notes on Pig Latin and its features.

  4. Explain Hive architecture and its query language (HiveQL).

  5. Differentiate between Pig and Hive.

  6. What is HBase? Describe its architecture and advantages.

  7. Explain the role of Zookeeper in Big Data systems.

  8. What is IBM Infosphere BigInsights?

  9. Discuss the importance of integrating multiple frameworks for Big Data analytics.



Module 5: Predictive Analytics (14 Questions)

  1. Define predictive analytics and its importance in data science.

  2. Explain the steps involved in building a predictive model.

  3. What is simple linear regression? Explain with an example.

  4. Define multiple linear regression and discuss its applications.

  5. Explain the interpretation of regression coefficients.

  6. Discuss model evaluation techniques in predictive analytics.

  7. What are the main visualization techniques used in analytics?

  8. Explain how predictive analytics is applied in business intelligence.

  9. Describe interaction techniques used in visualization.

  10. What are predictive systems? Explain their role in automation.

  11. Explain the challenges in implementing predictive analytics.

  12. Discuss statistical analysis techniques used in predictive modeling.

  13. Differentiate between descriptive, diagnostic, and predictive analytics.

  14. Write a case study showing real-world use of predictive analytics in finance or healthcare.

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