Hadoop

1. Introduction to BigData
        o Which data are called BigData
        o What are business use cases for BigData
        o Requirement of BigData for traditional Data warehousing and BI space
        o BigData solutions

2. Introduction to Hadoop
        o Amount of data processing in today's life
        o What is Hadoop why it is important
        o Comparison of Hadoop with traditional systems
        o History of Hadoop
        o Main components of Hadoop and its architecture

3. Hadoop Distributed File System (HDFS)
        o HDFS overview and design
        o HDFS architecture
        o HDFS file storage
        o Component failures and recoveries
        o Block placement
        o Balancing the Hadoop cluster

4. Hadoop Deployment
        o Different types of Hadoop deployment
        o Hadoop distribution options
        o Hadoop competitors
        o Procedure of Hadoop installation
        o Distributed cluster architecture
        o Lab: Hadoop Installation

5. Working with HDFS
        o Ways to access data in HDFS
        o Common operations and commands of HDFS
        o Different HDFS commands
        o Internals of a file read in HDFS
        o Data copying with 'distcp'
        o Lab: Working with HDFS

6. Hadoop Cluster Configuration
        o Configuration overview of Hadoop and the important configuration files
        o Parameters and values for configuration
        o Parameters of HDFS
        o MapReduce parameters
        o Environment setup of Hadoopv         o 'Include' and 'Exclude' configuration files
        o Lab: MapReduce Performance Tuning

7. Hadoop Administration and Maintenance
        o Directory structures and files of Namenode/Datanode
        o Filesystem image and Edit log
        o The Checkpoint Procedure
        o Namenode failure and procedure for recovery
        o Safe Mode
        o Metadata and Data backup
        o Potential problems and solutions / What to look for
        o Adding and removing nodes
        o Lab: MapReduce Filesystem Recovery

8. Job Scheduling
        o How to schedule Hadoop Jobs on the same cluster
        o Default Hadoop FIFO Schedule
        o Fair Scheduler and its configuration

9. Map-Reduce Abstraction
        o What MapReduce is and why is it popular
        o The Big Picture of the MapReduce
        o MapReduce process and terminology
        o MapReduce components, failures and recoveries
        o Working with MapReduce
        o Lab: Working with MapReduce

10. Programming MapReduce Jobs
        o Java MapReduce implementation
        o Map() and Reduce() methods
        o Code for calling Java MapReduce
        o Lab: Programming Word Count

11. Input/Output Formats and Conversion Between Different Formats
        o Default Input and Output formats
        o Sequence File structure
        o Sequence File Input and Output formats
        o Sequence File access via Java API and HDS
        o MapFile
        o Lab: Input Format
        o Lab: Format Conversion

12. MapReduce Features
        o Joining Data Sets in MapReduce Jobs
        o How to write a Map-Side Join
        o How to write a Reduce-Side Join
        o MapReduce Counters
        o Built-in and user-defined counters
        o Retrieving MapReduce counters
        o Lab: Map-Side Join
        o Lab: Reduce-Side Join

13. Introduction to Hive, Hbase, Flume, Sqoop, Oozie and Pig
        o Hive as a data warehouse infrastructure
        o Hbase as the Hadoop Database
        o Using Pig as a scripting language for Hadoop

14. Hadoop Case studies
        o How different organizations are using Hadoop cluster in their infrastructure

1. Ecommerce Project Similar as Amazon
2. Social Media project similar to Facebook
3. Hospital Management similar to Fortis

Visual Studio Code and Sublime

Training Materials written by Rajesh Upadhyay will be shared as a soft copy

Interview Preparation Includes :
        o coding test
        o soft skills
        o question and answer list prepared by RKCP