1. Introduction

1.1 Big Data Introduction

What is Big Data

Data Analytics

Bigdata Challenges

Technologies supported by big data

1.2 Hadoop Introduction

What is Hadoop?

History of Hadoop

Basic Concepts

Future of Hadoop

The Hadoop Distributed File System

Anatomy of a Hadoop Cluster

Breakthroughs of Hadoop

Hadoop Distributions:

Apache Hadoop

Cloudera Hadoop

Horton Networks Hadoop

MapR Hadoop

2. Hadoop Daemon Processes

Name Node

DataNode

Secondary Name Node/High Availability

Job Tracker/Resource Manager

Task Tracker/Node Manager

3. HDFS (Hadoop Distributed File System)

Blocks and Input Splits

Data Replication

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Hadoop Rack Awareness

Cluster Architecture and Block Placement

Accessing HDFS

JAVA Approach

CLI Approach

4. Hadoop Installation Modes and HDFS

Local Mode

Pseudo-distributed Mode

Fully distributed mode

Pseudo Mode installation and configurations

HDFS basic file operations

5. Hadoop Developer Tasks

5.1 Writing a MapReduce Program

Basic API Concepts

The Driver Class

The Mapper Class

The Reducer Class

The Combiner Class

The Partitioner Class

Examining a Sample MapReduce Program with several examples

Hadoop's Streaming API

Examining a Sample MapReduce Program with several examples

Running your MapReduce program on Hadoop 1.0

Running your MapReduce Program on Hadoop 2.0

5.2 Performing several Hadoop jobs

Sequence Files

Record Reader

Record Writer

Role of Reporter

Output Collector

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Processing XML files

Counters

Directly Accessing HDFS

ToolRunner

Using The Distributed Cache

5.3 Advanced MapReduce Programming

A Recap of the MapReduce Flow

The Secondary Sort

Customized Input Formats and Output Formats

Map-Side Joins

Reduce-Side Joins

5.4 Practical Development Tips and Techniques

Strategies for Debugging MapReduce Code

Testing MapReduce Code Locally by Using LocalJobRunner

Testing with MRUnit

Writing and Viewing Log Files

Retrieving Job Information with Counters

Reusing Objects

5.5 Data Input and Output

Creating Custom Writable and Writable-Comparable Implementations

Saving Binary Data Using SequenceFile and Avro Data Files

Issues to Consider When Using File Compression

5.6 Tuning for Performance in MapReduce

Reducing network traffic with Combiner, Partitioner classes

Reducing the amount of input data using compression

Reusing the JVM

Running with speculative execution

Input Formatters

Output Formatters

Schedulers

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FIFO schedulers

FAIR Schedulers

CAPACITY Schedulers

5.7 YARN

What is YARN

How YARN Works

Advantages of YARN

6. Hadoop Ecosystems

6.1 PIG

PIG concepts

Install and configure PIG on a cluster

PIG Vs MapReduce and SQL

PIG Vs HIVE

Write sample PIG Latin scripts

Modes of running PIG

Programming in Eclipse

Running as Java program

PIG UDFs

PIG Macros

Accessing Hive from PIG

6.2 HIVE

Hive concepts

Hive architecture

Installing and configuring HIVE

Managed tables and external tables

Partitioned tables

Bucketed tables

Complex data types

Joins in HIVE

Multiple ways of inserting data in HIVE tables

CTAS, views, alter tables

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User-defined functions in HIVE

Hive UDF

Hive UDAF

Hive UDTF

6.3 SQOOP

SQOOP concepts

SQOOP architecture

Install and configure SQOOP

Connecting to RDBMS

Internal mechanism of import/export

Import data from Oracle/Mysql to HIVE

Export data to Oracle/Mysql

Other SQOOP commands

6.4 HBASE

HBASE concepts

ZOOKEEPER concepts

HBASE and Region server architecture

File storage architecture

NoSQL vs SQL

Defining Schema and basic operations

DDLs

DMLs

HBASE use cases

Access data stored in HBASE using clients like CLI, and Java

Map Reduce client to access the HBASE data

HBASE admin tasks

6.5 OOZIE

OOZIE concepts

OOZIE architecture

Workflow engine

Job coordinator

Install and configuring OOZIE

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HPDL and XML for creating Workflows

Nodes in OOZIE

Action nodes

Control nodes

Accessing OOZIE jobs through CLI, and web console

Develop sample workflows in OOZIE on various Hadoop distributions

Run HDFS file operations

Run MapReduce programs

Run PIG scripts

Run HIVE jobs

Run SQOOP Imports/Exports

6.6 FLUME

FLUME Concepts

FLUME architecture

Installation and configurations

Executing FLUME jobs

6.7 IMPALA

What is Impala

How Impala Works

Impala Vs Hive

Impala's shortcomings

Impala Hands-on

6.8 ZOOKEEPER

ZOOKEEPER Concepts

Zookeeper as a service

Zookeeper in production

7. Integrations

Mapreduce and HIVE integration

Mapreduce and HBASE integration

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Java and HIVE integration

HIVE - HBASE Integration

SAS – HADOOP

8. Spark

Introduction to Scala

Functional Programming in Scala

Working with Spark RDDs

9. Hadoop Administrative Tasks:

Setup Hadoop cluster: Apache, Cloudera and VMware

Install and configure Apache Hadoop on a multi-node cluster

Install and configure Cloudera Hadoop distribution in fully distributed mode

Install and configure different ecosystems

Basic Administrative tasks

10. Course Deliverables

Workshop style coaching

Interactive approach

Course material

Hands-on practice exercises for each topic

Quiz at the end of each major topic

Tips and techniques on Cloudera Certification Examination

Linux concepts and basic commands

On-Demand Services

Mock interviews for each individual will be conducted on a need basis

SQL basics on need basis

Core Java concepts on need basis

Resume preparation and guidance

Interview questions