• Introduction to Big Data and Analytics
• Introduction to Hadoop
• Hadoop ecosystem - Concepts
• Hadoop Map-reduce concepts and features
• Developing the map-reduce Applications
• Pig concepts
• Hive concepts
• Sqoop concepts
• Flume Concepts
• Oozie workflow concepts
• Impala Concepts
• Hue Concepts
• HBASE Concepts
• ZooKeeper Concepts
• Real Life Use Cases

Reporting Tool

• Tableau

1. Virtualbox/VM Ware

• Basics
• Installations
• Backups
• Snapshots

2. Linux

• Basics
• Installations
• Commands

3. Hadoop

• Why Hadoop?
• Scaling
• Distributed Framework
• Hadoop v/s RDBMS
• Brief history of hadoop

4. Setup hadoop

• Pseudo mode
• Cluster mode
• Ipv6
• Ssh
• Installation of java, hadoop
• Configurations of hadoop
• Hadoop Processes ( NN, SNN, JT, DN, TT)
• Temporary directory
• UI
• Common errors when running hadoop cluster, solutions

5. HDFS- Hadoop distributed File System

• HDFS Design and Architecture
• HDFS Concepts
• Interacting HDFS using command line
• Interacting HDFS using Java APIs
• Dataflow
• Blocks
• Replica

6. Hadoop Processes

• Name node
• Secondary name node
• Job tracker
• Task tracker
• Data node

7. Map Reduce

• Developing Map Reduce Application
• Phases in Map Reduce Framework
• Map Reduce Input and Output Formats
• Advanced Concepts
• Sample Applications
• Combiner

8. Joining datasets in Mapreduce jobs

• Map-side join
• Reduce-Side join

9. Map reduce customization

• Custom Input format class
• Hash Partitioner
• Custom Partitioner
• Sorting techniques
• Custom Output format class

10. Hadoop Programming Languages :-

I.HIVE

• • Introduction
  • Installation and Configuration
  • Interacting HDFS using HIVE
  • Map Reduce Programs through HIVE
  • HIVE Commands
  • Loading, Filtering, Grouping
  • Data types, Operators
  • Joins, Groups
  • Sample programs in HIVE

II. PIG

• • Basics
  • Installation and Configurations
  • Commands

OVERVIEW HADOOP DEVELOPER

11. Introduction

12. The Motivation for Hadoop

• Problems with traditional large-scale systems
• Requirements for a new approach

13. Hadoop: Basic Concepts

• An Overview of Hadoop
• The Hadoop Distributed File System
• Hands-On Exercise
• How MapReduce Works
• Hands-On Exercise
• Anatomy of a Hadoop Cluster
• Other Hadoop Ecosystem Components

14. Writing a MapReduce Program

• The MapReduce Flow
• Examining a Sample MapReduce Program
• Basic MapReduce API Concepts
• The Driver Code
• The Mapper
• The Reducer
• Hadoop's Streaming API
• Using Eclipse for Rapid Development
• Hands-on exercise
• The New MapReduce API

15. Common MapReduce Algorithms

• Sorting and Searching
• Indexing
• Machine Learning With Mahout
• Term Frequency Inverse Document Frequency
• Word Co-Occurrence
• Hands-On Exercise.

16.PIG Concepts.

• Data loading in PIG.
• Data Extraction in PIG.
• Data Transformation in PIG.
• Hands on exercise on PIG.

17. Hive Concepts.

• Hive Query Language.
• Alter and Delete in Hive.
• Partition in Hive.
• Indexing.
• Joins in Hive.Unions in hive.
• Industry specific configuration of hive parameters.
• Authentication & Authorization.
• Statistics with Hive.
• Archiving in Hive.
• Hands-on exercise

18. Working with Sqoop

• Introduction.
• Import Data.
• Export Data
• Sqoop Syntaxs.
• Databases connection.
• Hands-on exercise

19. Working with Flume

• Introduction.
• Configuration and Setup.
• Flume Sink with example.
• Channel.
• Flume Source with example.
• Complex flume architecture.

20. OOZIE Concepts
21. IMPALA Concepts
22. HUE Concepts
23. HBASE Concepts
24. ZooKeeper concepts

Reporting Tool

Tableau

Course Topics
Overview

What is visual analysis?
Strengths/weakness of the visual system.

Laying the Groundwork for Visual Analysis
Analytical Process
Preparing for analysis

Getting, Cleaning and Classifying Your Data
Cleaning, formatting and reshaping.
Using additional data to support your analysis.
Data classification

Visual Mapping Techniques
Visual Variables : Basic Units of Data Visualization
Working with Color
Marks in action: Common chart types

Solving Real-World Problems with Visual Analysis
Getting a Feel for the Data- Exploratory Analysis.
Making comparisons
Looking at (co-)Relationships.
Checking progress.
Spatial Relationships.
Try, try again.

Communicating Your Findings
Fine-tuning for more effective visualization
Storytelling and guided analytics
Dashboards