Big Data Fundamental


Big Data Fundamental

Associations currently approach gigantic measures of information and it's affecting the manner in which they work. They are acknowledging with a specific end goal to be fruitful they should use their information to settle on viable business choices.

In this course, some portion of the Big Data MicroMasters program, you will figure out how enormous information is driving hierarchical change and the key difficulties associations confront when endeavoring to break down huge informational indexes.

You will learn key systems, for example, information mining and stream handling. You will likewise figure out how to outline and execute PageRank calculations utilizing MapReduce, a programming worldview that considers gigantic versatility crosswise over hundreds or thousands of servers in a Hadoop bunch. You will figure out how huge information has enhanced web inquiry and how web based publicizing frameworks function.

Before the finish of this course, you will have a superior comprehension of the different utilizations of enormous information strategies in industry and research.

What you'll learn

  • Knowledge and application of MapReduce
  • Understanding the rate of occurrences of events in big data
  • How to design algorithms for stream processing and counting of frequent elements in Big Data
  • Understand and design PageRank algorithms
  • Understand underlying random walk algorithms
Sr No Topic Detail Topic
      1 Understanding Big Data and Hadoop 1. Introduction to Big Data & Big Data Challenges.
2. Limitations & Solutions of Big Data Architecture.
3. Hadoop & its Features.
4. Hadoop Ecosystem.
      2 Hadoop Architecure and HDFS  1. Understanding Hadoop Architecture.
2. Hadoop Cluster Modes.
3. Understanding HDFS
       3 Hadoop MapReduce Framework 1. Traditional vs MapReduce.
2. Why MapReduce.
3. YARN Components.
4. YARN Architecure.
       4 Apache Pig 1. Introduction to Apache Pig.
2. Map Reduce vs Pig.
3. Pig Components & Pig Execution.
4. Pig Data Types & Data Models in Pig.
        5 Apache Hive 1. Introduction to Apache Hive.
2. Hive vs Pig.
3. Hive Architecure and Components.
4. Limitation of Hive.
5. Comparison with Traditional Database.
6. Hive Partition.
7. Hive Tables.
8. Importing Data.


Distributed Data Processing with Spark 1. Understanding Spark.
2. Understanding Spark Ecosystem.
3. Spark Components.