Big Data Hadoop Course Syllabus

1: Hadoop Introduction

Introduction to Data and System
Types of Data
Traditional way of dealing large data and its problems
Types of Systems & Scaling
What is Big Data
Challenges in Big Data
Challenges in Traditional Application
New Requirements
What is Hadoop? Why Hadoop?
Brief history of Hadoop
Features of Hadoop
Hadoop and RDBMS
Hadoop Ecosystem’s overview

2: Hadoop Installation

Installation in detail
Creating Ubuntu image in VMware
Downloading Hadoop
Installing SSH
Configuring Hadoop, HDFS & MapReduce
Download, Installation & Configuration Hive
Download, Installation & Configuration Pig
Download, Installation & Configuration Sqoop
Download, Installation & Configuration Hive
Configuring Hadoop in Different Modes

3: Hadoop Distribute File System (HDFS)

File System - Concepts
Blocks
Replication Factor
Version File
Safe mode
Namespace IDs
Purpose of Name Node
Purpose of Data Node
Purpose of Secondary Name Node
Purpose of Job Tracker
Purpose of Task Tracker
HDFS Shell Commands – copy, delete, create directories etc.
Reading and Writing in HDFS
Difference of Unix Commands and HDFS commands
Hadoop Admin Commands
Hands on exercise with Unix and HDFS commands
Read / Write in HDFS – Internal Process between Client, NameNode & DataNodes
Accessing HDFS using Java API
Various Ways of Accessing HDFS
Understanding HDFS Java classes and methods
Commissioning / DeCommissioning DataNode
Balancer
Replication Policy
Network Distance / Topology Script

4: Map Reduce Programming

About MapReduce
Understanding block and input splits
MapReduce Data types
Understanding Writable
Data Flow in MapReduce Application
Understanding MapReduce problem on datasets
MapReduce and Functional Programming
Writing MapReduce Application
Understanding Mapper function
Understanding Reducer Function
Understanding Driver
Usage of Combiner
Usage of Distributed Cache
Passing the parameters to mapper and reducer
Analysing the Results
Log files
Input Formats and Output Formats
Counters, Skipping Bad and unwanted Records
Writing Join’s in MapReduce with 2 Input files. Join Types
Execute MapReduce Job - Insights
Exercise’s on MapReduce

5: Hive

Hive concepts
Hive architecture
Install and configure hive on cluster
Different type of tables in hive
Hive library functions
Buckets
Partitions
Joins in hive
Inner joins
Outer Joins
Hive UDF
Hive Query Language

6: PIG

Pig basics
Install and configure PIG on a cluster
PIG Library functions
Pig Vs Hive
Write sample Pig Latin scripts
Modes of running PIG
Running in Grunt shell
Running as Java program
PIG UDFs

7: Sqoop

Install and configure Sqoop on cluster
Connecting to RDBMS
Installing Mysql
Import data from Mysql to hive
Export data to Mysql
Internal mechanism of import/export

8: HBase

HBase concepts
HBase architecture
Region server architecture
File storage architecture
HBase basics
Column access
Scans
HBase use cases
Install and configure HBase on a multi node cluster
Create database, Develop and run sample applications
Access data stored in HBase using Java API
Map Reduce client to access the HBase data

9: YARN

Resource Manager (RM)
Node Manager (NM)
Application Master (AM)

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Students Feedback for Big Data Training in Gandhinagar

Jainsy Anjirwala

I thank Apponix for offering the best online training experience. It was very informative. The training helped a lot in my career.

Big Data Expert

Jaya Jayasree

The classes are too good. The trainer did his best in explaining the topics. The subject is made clear through enough examples and methods. Thanks a lot sir.

Big Data Analyst

Kajal Thakkar

Excellent learning experience. I recommend others too to join Apponix to have the best online technical trainings. Well organized classes.

Big Data Analyst

Mr. Srikanth

8+ years of working experience in MNC

Big Data Hadoop Trainer Profile

Reputed professional with 12+ years of experience in Big Data
Trained 1500+ students
Top rated trainer of Apponix
Expert level experience in:
Efficient code writing which is reusable and testable
Designing and implementing low-latency, high-availability, and performance applications
Enhancing the functionality of existing systems and implementing solutions for security and data protection
Well-informed and practical experience in the advanced concepts and applications od Big Data Hadoop
Detail oriented training, good communication skills and noted for subject delivery.

Big Data Hadoop Instructor Experience

Writing reusable, testable, and efficient code
Designing and implementation of low-latency, high-availability, and performance applications.
Integration of user-facing elements developed by front-end developers with server side logic.
Integration of data storage solutions includes databases, key-value stores, blob stores, etc.
Developing back-end components to improve responsiveness and overall performance
Integrating user-facing elements into applications
Improving functionality of existing systems
Implementing security and data protection solutions

Student Review

Kalpita Pakhare

Good trainer and helped a lot. He explained many real-time scenarios to understand the topics. Had a good learning experience..

Khalid Naik

The way of teaching was very effective. The trainer supported very much. Complex concepts are taught in a very simple way.

Kiran Maibr

Apponix was the best choice I have made. Good knowledge about the advanced technicalities was made. By the completion of the course, we will get a good idea about the basics and advanced technologies.

Krishna Veni

i completed SEO course here .Akash sir is an excellent trainer! I had a great experience learning here!

Kshira Poral

Apponix the one of the best institute for Digital Marketing and SEO. The way of teaching by Akash sir is fantastic, here i learned many things.

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Why Should You Learn Big Data?

Most demanding and promising career option
Big Data find its implementation in new areas each day. And its implementation in the fields of AI, Robotics, Web Development, DevOps and so on, has considerably increased the credibility and demand of Big Data Hadoop professionals
Most preferred, premier and flexible language which is of open source form