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HADOOP DEVELOPER TRAININGENROLL NO Course duration :30 hours Course fee : 35000 1. INTRODUCTION A. Distributed computing B. Cloud Computing C. Data Past, Present and Future D. Computing Past, Present and Future E. Hadoop F. NoSQL 2. UNDERSTANDING HADOOP STACK A. MapReduce B. NoSQL C. CAP Theorem D. Databases: Key Value, Document, E. Graph F. HBase and Cassandra G. Hive and Pig H. HDFS 3. UNDERSTANDING DATA A. Data collection and generation B. Data Storage C. Data Retrieval D. Random Access vs. Sequential Access E. Disk is the new Tape The Return of F. sequential access to data 4. HANDS ON HADOOP A. Hadoop Setup Single Node B. Cluster Nodes C. HDFS File System D. HBase Setup E. HBase Shell 5. HANDS ON TO DATA

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Page 1: Hadoop developer online training

HADOOP DEVELOPER TRAININGENROLL NO

Course duration :30 hours

Course fee : 35000

1. INTRODUCTION

A. Distributed computing

B. Cloud Computing

C. Data Past, Present and Future

D. Computing Past, Present and Future

E. Hadoop

F. NoSQL

2. UNDERSTANDING HADOOP STACK

A. MapReduce

B. NoSQL

C. CAP Theorem

D. Databases: Key Value, Document,

E. Graph

F. HBase and Cassandra

G. Hive and Pig

H. HDFS

3. UNDERSTANDING DATA

A. Data collection and generation

B. Data Storage

C. Data Retrieval

D. Random Access vs. Sequential Access

E. Disk is the new Tape – The Return of

F. sequential access to data

4. HANDS ON HADOOP

A. Hadoop Setup Single Node

B. Cluster Nodes

C. HDFS File System

D. HBase Setup

E. HBase Shell

5. HANDS ON TO DATA

Page 2: Hadoop developer online training

A. Working on Twitter Data

B. Importing data into HBase

C. Querying using HBase CLI

D. Understanding data organization

E. Hbase Internals

F. Exercise: Solving various query using

G. scan and get

6. INTRODUCTION TO HBASE

A. Unlearning SQL

B. Learning NoSQL

C. Creating Table

D. Understanding Column Families

E. Unlearning Entity Relation

F. Learning Column Value & Key Pair

G. Unlearning Index & Query

H. Learning Scan and Scan Only

7. INTRODUCTION TO HIVE

8. INTRODUCTION TO PIG

9. LOOKING DATA AS LARGE SPARSE MATRIX

10. APPLYING BI ON LARGE DATA

11. EXERCISE: WORKING WITH TWITTER DATA

12. EXERCISE: WORKING WITH FACEBOOK DATA

13. EXERCISE: WORKING WITH LINKEDIN DATA