Conflict of Interest

High Risk Vendors

High Risk Distributors

High Risk Guests and Attendees

Big Data & Hadoop Module 1

Hadoop (Big Data) Ecosystem

Motivation for Hadoop

Different types of projects by Apache

Role of projects in the Hadoop Ecosystem

Key technology foundations required for Big Data

Limitations and Solutions of existing Data Analytics Architecture

Comparison of traditional data management systems with Big Data management systems

Evaluate key framework requirements for Big Data analytics

Hadoop Ecosystem & Hadoop 2.x core components

Explain the relevance of real-time data

Explain how to use big and real-time data as a Business planning tool

Ghost employees

Employee falsified time entry

Background Checks

Segregation of Duties

Big Data & Hadoop Module 2

Building Blocks

Quick tour of Java

Quick tour of Linux commands

Introduction to Cloudera VM/Cloudera Manager(Apache Ambari)/Download & usage instruction

Ghost Vendoors

Duplicate vendor transactions

Vendor Spend Analysis

Vendor Price Variations

Big Data & Hadoop Module 3

Hadoop Cluster -Architecutre - Configuration files

Hadoop Master-Slave Architecture

The Hadoop Distributed File System - data storage

Explain different types of cluster setups(Fully distributed/Pseudo etc)

Hadoop Cluster setup - Installation

Hadoop 2.x Cluster Architecture

A Typical enterprise cluster – Hadoop Cluster Modes

Big Data & Hadoop Module 4

Data Integration using Sqoop, Flume & Talend

Integrating Hadoop into an existing Enterprise

Loading Data from an RDBMS into HDFS by Using Sqoop

Managing Real-Time Data Using Flume

Accessing HDFS from Legacy Systems with FuseDFS and HttpFS

Introduction to Talend (community system)

Data loading to HDFS using Talend

Ghost Customers

Unfavorable contracts terms

Repeated orders followed by returns

Excessive customer comission payments

Big Data & Hadoop Module 5

Data Analysis using PIG

Introduction to Hadoop Data Analysis Tools

Introduction to PIG - MapReduce Vs Pig, Pig Use Cases

Pig Latin Program & Execution

Use Pig to automate the design and implementation of MapReduce applications

Data Analysis using PIG

Big Data & Hadoop Module 6

Data Analysis using HIVE

Introduction to Hive - Hive Vs. PIG - Hive Use Cases

Discuss the Hive data storage principle

Explain the File formats and Records formats supported by the Hive environment

Perform operations with data in Hive

Hive QL: Joining Tables, Dynamic Partitioning, Custom MapReduce Scripts

Hive Script, Hive UDF

Ghost Customers

Unfavorable contracts terms

Repeated orders followed by returns

Excessive customer comission payments

Big Data & Hadoop Module 7

Data Analysis using Impala

Introduction to Impala & Architecture

How Impala executes Queries and its importance

Hive vs. PIG vs. Impala

Extending impala with User Defined functions

Improving impala Performance

Big Data & Hadoop Module 8

NoSql Database - HBASE

Introduction to NoSQL Databases and Hbase

HBase v/s RDBMS, HBase Components, HBase Architecture

HBase Cluster Deployment

Ghost Customers

Unfavorable contracts terms

Repeated orders followed by returns

Excessive customer comission payments

Big Data & Hadoop Module 9

Hadoop - Other Analytics Tools

Introduction to the role of R in Hadoop Ecosystem

Introduction to Jasper Reports & creating reports by integrating with Hadoop

Role of Kafka & Avro in real projects

Big Data & Hadoop Module 10

Hadoop Core Components - HDFS & Map Reduce(YARN)

HDFS Overview & Data storage in HDFS

Get the data into Hadoop from local machine (Data Loading Techniques) - vice versa

MapReduce Overview (Traditional way Vs. MapReduce way)

HDFS Overview & Data storage in HDFS

Concept of Mapper & Reducer

Understanding MapReduce program skeleton

Running MapReduce job in Command line/Eclipse

Develop MapReduce Program in JAVA

Develop MapReduce Program with the streaming API

Test and debug a MapReduce program in the design time

How Partitioners and Reducers Work Together

Writing Customer Partitioners Data Input and Output

Creating Custom Writable and Writable Comparable Implementations

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