{"id":29372,"date":"2025-04-14T17:48:19","date_gmt":"2025-04-14T12:18:19","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/315320-big-data-technology-syllabus-for-cloud-computing-big-data-5th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T17:48:19","modified_gmt":"2025-04-14T12:18:19","slug":"315320-big-data-technology-syllabus-for-cloud-computing-big-data-5th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/315320-big-data-technology-syllabus-for-cloud-computing-big-data-5th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"315320: Big Data Technology Syllabus for Cloud Computing & Big Data 5th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Big Data Technology detailed Syllabus for Cloud Computing & Big Data (BD), K scheme PDF has been taken from the MSBTE<\/a> official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical\/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below. <\/p>\n

For all other MSBTE Cloud Computing & Big Data 5th Sem K Scheme Syllabus PDF, do visit MSBTE Cloud Computing & Big Data 5th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for big data technology is as follows.<\/p>\n

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

    \n
  1. Illustrate characteristics and technologies of Big Data.<\/li>\n
  2. Apply relevant Big Data Storage techniques for a given application.<\/li>\n
  3. Use Big Data Technologies Hive and Pig to handle different nature of data.<\/li>\n
  4. Implement Big Data Processing Technique for a given application.<\/li>\n
  5. Apply Data Visualization Techniques for a given application.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Introduction to Big Data Technologies 1.1\tIntroduction :Definition of Big Data, Characteristics of big data, types of big data 1.2\tApplications of big data 1.3\tDifference between big data and traditional data in terms of volume, velocity, variety, veracity, value, scalability 1.4\tBig data technology-definition, types : operational and analytical 1.5\tClassification of big data technologies based on its purpose: Data Storage, Data Mining, Data Analytics, Data Visualization\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Videos Collaborative Learning\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Handling data and Query with Hive and pig 3.1\tApache Hive: Hive architecture, warehouse directory and megastore, Hive data types, loading data into tables, Hive file formats, Hive built in functions, Querying data, Sorting, Aggregation 3.2\tApache Pig: Architecture, execution modes, built-in functions, Pig operations: group and join, combine and split, filter\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Video Demonstrations Collaborative learning Hands-on\n<\/p>\n

    Unit IV<\/h4>\n

    Big Data Processing Tools 4.1\tApache Spark: Need for Apache Spark, Apache Spark architecture, Spark components, Resilient Distributed Datasets(RDD) transformations 4.2\tSpark SQL, Spark Session, Data Frame operations 4.3\tApache Kafka: Need, benefits, Kafka Messaging System: Point to point and Public subscribe messaging system, Kafka cluster architecture\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Video Demonstrations Collaborative learning Hands-on\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. *Write a python program for analyzing the characteristics of any large dataset case study<\/li>\n
    2. *Install Single-node Hadoop cluster<\/li>\n
    3. *Write HDFS commands for file operations<\/li>\n
    4. *Write MapReduce program for word count operation<\/li>\n
    5. Write MapReduce program for matrix addition<\/li>\n
    6. Write MapReduce program to find max temperature<\/li>\n
    7. *Write MapReduce program to sort array elements<\/li>\n
    8. Write basic queries for CRUD operations in MongoDB<\/li>\n
    9. *Hive installation and Queries<\/li>\n
    10. Create Hive table with different storage format specification<\/li>\n
    11. Apply Hive QL clauses<\/li>\n
    12. Apache Pig: *a)Install Apache Pig *b)Use Pig operators<\/li>\n
    13. *Develop spark program to perform basic operations on data frames using python<\/li>\n
    14. Write a program to read and write data stored in Apache hive through Spark SQL<\/li>\n
    15. Develop a program to perform word count operation<\/li>\n
    16. *Write Program to perform RDD transformations<\/li>\n
    17. *Develop program to produce and consume message and build simple pipeline<\/li>\n
    18. *Write program to visualize data using verious plots using Matplotlib<\/li>\n
    19. *Write program to visualize data using verious plots using Seaborn<\/li>\n
    20. Develop program to create dashboard using plotly tool<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Micro Project<\/i><\/p>\n