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Big Data Computing

Prof. Rajiv Misra · IIT Patna

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About

In today's fast-paced digital world , the incredible amount of data being generated every minute has grown tremendously from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and GPS signals from cell phone to name a few. This amount of large data with different velocities and varieties is termed as big data and its analytics enables professionals to convert extensive data through statistical and quantitative analysis into powerful insights that can drive efficient decisions. This course provides an in-depth understanding of terminologies and the core concepts behind big data problems, applications, systems and the techniques, that underlie today's big data computing technologies. It provides an introduction to some of the most common frameworks such as Apache Spark, Hadoop, MapReduce, Large scale data storage technologies such as in-memory key/value storage systems, NoSQL distributed databases, Apache Cassandra, HBase and Big Data Streaming Platforms such as Apache Spark Streaming, Apache Kafka Streams that has made big data analysis easier and more accessible. And while discussing the concepts and techniques, we will also look at various applications of Big Data Analytics using Machine Learning, Deep Learning, Graph Processing and many others. The course is suitable for all UG/PG students and practicing engineers/ scientists from the diverse fields and interested in learning about the novel cutting edge techniques and applications of Big Data Computing.

PREREQUISITES : Data Structure & Algorithms, Computer Architecture, Operating System, Database Management Systems

INDUSTRY SUPPORT : Companies like Amazon, Microsoft, Google, IBM, Facebook

Lectures

Week 1

  1. Introduction to Big Data YouTube
  2. Big Data Enabling Technologies YouTube
  3. Hadoop Stack for Big Data YouTube

Week 2

  1. Hadoop Distributed File System (HDFS) YouTube
  2. Hadoop MapReduce 1.0 YouTube
  3. Hadoop MapReduce 2.0 (Part-I) YouTube
  4. Hadoop MapReduce 2.0 (Part-II) YouTube
  5. MapReduce Examples YouTube

Week 3

  1. Parallel Programming with Spark YouTube
  2. Introduction to Spark YouTube
  3. Spark Built-in Libraries YouTube
  4. Design of Key-Value Stores YouTube

Week 4

  1. Data Placement Strategies YouTube
  2. CAP Theorem YouTube
  3. Consistency Solutions YouTube
  4. CQL (Cassandra Query Language) YouTube
  5. Design of Zookeeper YouTube

Week 5

  1. Design of HBase YouTube
  2. Spark Streaming and Sliding Window Analytics (Part-I) YouTube
  3. Spark Streaming and Sliding Window Analytics (Part-II) YouTube
  4. Sliding Window Analytics YouTube
  5. Introduction to Kafka YouTube

Week 6

  1. Big Data Machine Learning (Part-I) YouTube
  2. Big Data Machine Learning (Part-II) YouTube
  3. Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics YouTube
  4. Parallel K-means using Map Reduce on Big Data Cluster Analysis YouTube

Week 7

  1. Decision Trees for Big Data Analytics YouTube
  2. Big Data Predictive Analytics (Part-I) YouTube
  3. Big Data Predictive Analytics (Part-II) YouTube

Week 8

  1. Parameter Servers YouTube
  2. PageRank Algorithm in Big Data YouTube
  3. Spark GraphX & Graph Analytics (Part-I) YouTube
  4. Spark GraphX & Graph Analytics (Part-II) YouTube
  5. Case Study: Flight Data Analysis using Spark GraphX YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Patna
Department
Computer Science and Engineering
Teacher
Prof. Rajiv Misra
Level
Graduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Kanpur.
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