Notes of Big Data – Elective II

The following chapter-wise notes of Big Data (Elective II for BE Computer and Electronics) are prepared by Dinesh Amatya. The syllabus along with marking scheme is available on IOE Syllabus of Big Data Technologies page. The notes/slides in pdf format covers most of the parts of the syllabus. Click on download link of each chapter and save to read the notes offline.

  1. Introduction to Big Data – 7 hours – 12 marks
  2. Google File System – 7 hours – 13 marks
  3. Map Framework – 10 hours – 18 marks
  4. NoSQL – 6 hours – 11 marks
  5. Searching and Indexing Big Data – 7 hours – 13 marks
  6. Case Study Hadoop and Elastic Search – 8 hours – 13 marks

Pulchowk Notes

The notes provided for students of Pulchowk Campus can be accessed from this link. It contains pdf notes and ppt of Hadoop, Big Table, GFS, GFS MapReduce, NoSQL etc.

KEC Notes

The study materials for Big Data Technologies provided in KEC consists of mostly powerpoint slides. Download chapter wise ppt of Big Data.

Additional Resources

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Raju Dawadi
Raju Dawadi
Raju is currently actively involved in DevOps world and is focused on Container based architecture & CI/CD automation along with Linux administration. Want to discuss with him on any cool topics? Feel free to connect on twitter, linkedIn, facebook.

3 Comments

  1. Shree Krishna Khanal says:

    chapter 6. Case Study Hadoop – 8 hours – 13 marks ko link mistake parecha case study ko link hunu parne ma ma IOE Syllabus of Electrical Engineering Material for BEX parecha #ioenote ko hit ma jari suchana

  2. […] Big Data Technologies (Subject Code: CT 765 07) falls under Elective II for BE Computer and Electronics & Communication Engineering. Big Data Technologies has 3 lectures, I Tutorial and 3/2 Practical is elective for Fourth Year – Second Part. The Course Objectives of introducing Big Data Technologies is to introduce the current scenarios of big data and provide various facets of big data and to be familiar with the technologies playing key role in it and equips them with necessary knowledge to use them for solving various big data problems in different domains. Read: Notes, manuals and powerpoint slides of Big Data […]

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