Introduction To Big Data & Hadoop Ecosystem

HDFS-Map Reduce

What you'll learn

Learners will develop a understanding of Big Data concepts, as well as the fundamental principles of the Hadoop ecosystem

Learners will have the opportunity to gain hands-on experience with HDFS commands on the Google Cloud Platform.

Learners will acquire knowledge and understanding of MapReduce

Learners will understand the learning about Hadoop architecture and core components, such as the Hadoop Distributed File System (HDFS)

Requirements

No programming language proficiency is necessary

Having a reliable internet connection and a strong desire to learn are essential prerequisites.

Description

This course provides in-depth coverage of essential topics related to Big Data and the Hadoop Ecosystem. It is highly recommended for individuals working in the IT industry, as well as aspiring Big Data professionals. The course encompasses a wide range of subjects, including an introduction to Big Data, the fundamentals of Big Data, the foundations of Big Data and the Hadoop Ecosystem, the Hadoop Distributed File System (HDFS), and MapReduce. By completing this course, learners will gain a solid understanding of these critical concepts and technologies, empowering them to excel in the field of Big Data. In this course, the primary focus is on teaching learners the skills and knowledge required to effectively work with the Big Data Hadoop ecosystem. Specifically, the course covers the following key topics:Hadoop Fundamentals: Learners will gain knowledge about the core concepts and architecture of Hadoop, including its distributed file system (HDFS) and the MapReduce processing framework.Hadoop Ecosystem Tools: The course delves into various tools and components of the Hadoop ecosystem, such as Hive, Pig, Spark, HBase, Sqoop, Flume, and Oozie. Learners will develop an understanding of these tools and their functionalities.MapReduce:Learners will also learn about scaling, fault tolerance, data storage and processing, the programming model, different types of nodes and their failures, and gain a brief understanding of the Hadoop Ecosystem.

Overview

Section 1: THE INTRODUCTION AND FUNDAMENTALS

Lecture 1 Data VS Information

Lecture 2 Data Storage and Processing

Lecture 3 Data Sources

Lecture 4 Big Data Introduction

Section 2: THE FOUNDATIONS OF BIG DATA

Lecture 5 Emergence of Big Data

Lecture 6 Basic Terminologies

Lecture 7 Central theme of Big Data

Lecture 8 Requirements of Programming Model

Lecture 9 Understand Distributed Processing through a Story

Section 3: ENVIRONMENT AND INSTALLATION

Lecture 10 Oracle_VirtualMachine_Installation

Lecture 11 How to install Ubuntu operating system on Virtual Box

Lecture 12 How to Setup Google Cloud Platform

Section 4: HADOOP ECOSYSTEM

Lecture 13 Introduction to Hadoop Ecosystem

Section 5: HADOOP DISTRIBUTED FILE SYSTEM

Lecture 14 What is HDFS?

Lecture 15 Nodes in HDFS

Lecture 16 Storing Files in HDFS

Lecture 17 Reading File from HDFS

Lecture 18 Challenges in Distributed Systems

Lecture 19 Managing the Data Node Failure

Lecture 20 Managing Name Node Failure

Lecture 21 HDFS Commands Part 1

Lecture 22 HDFS Commands Part 2

Section 6: MAP REDUCE

Lecture 23 Introduction to Map Reduce

Lecture 24 Map Reduce Flow Example 1

Lecture 25 Map Reduce Implementation

Lecture 26 Map Reduce Example 2 - User View Count

Lecture 27 Map Reduce Mappers and Reducers

Lecture 28 Shuffle-Sort-Partitions

Lecture 29 Map Reduce Combiners

Lecture 30 Combiner with Caution

Lecture 31 Map Reduce Wrap Up

Beginners who are interested in starting their journey in Big Data,Professionals who are considering a career in Big Data and individuals seeking knowledge in Big Data, specifically in the areas of Hadoop and HDFS