big data & hadoop workshop outlines
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big data and hadoop outlinesTRANSCRIPT
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2-Days Workshop Outlines
BIG DATA
AND
HADOOP
Highlights of Big Data & Hadoop workshop
Implement a Hadoop Project
Learn to write Complex MapReduce programs
Perform Data Analytics using Pig and Hive
Master the concepts of Hadoop Distributed File System and MapReduce Framework
Work on a Real Life Project on Big Data Analytics and gain Hands on Project
Experience
About Big Data & Hadoop
Hadoop is 100% open or free source, and pioneered a fundamentally new way of storing and processing
data. Instead of relying on expensive, proprietary hardware and different systems to store and process data,
Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-
standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is
too big. And in today's hyper-connected world where more and more data is being created every day,
Hadoop's breakthrough advantages mean that businesses and organizations can now find value in data that
was recently considered useless
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Registration Fees: - 1050 per student only (The fees include training, Certification and event
Registration and a free Big Data and Hadoop kit to each student.)
Day 1 (1st Session)
A. Introduction to Big Data and Hadoop
1. What is a Data?
2. Type of Data
3. Need of Big Data
4. Characteristics of Big Data
B. Different Components of Hadoop
C. Big Data Technology
1. Traditional IT approach
2. Big Data Capabilities
3. Milestones of Hadoop
Day 1 (2nd Session)
A. Software Introduction
1. VMware Player
2. VMware installed with BIOS system
3. Horton Works Sand Box Introduction
B. Hadoop Architecture
1. Hadoop cluster
2. Hadoop Core Services
3. Hadoop Core Components
4. Map reduce Introduction
5. HDFS
Day 2 (1st Session)
A. Pig
1. Introduction to Apache Pig
2. Components of pig
3. Map Reduce vs. Apache Pig
4. Different Data Types in Pig
5. Modes of Execution in Pig (Local Mode)
6. Execution Mechanism
7. Pig Commands
8. Examples Of pig
8.1 Word Count
8.2 Batting Examples
Day 2 (2nd Session)
A. Hive
1. Hive Introduction
2. Hive characteristics
3. System Architecture & components of hive
4. Query Compiler
5. SQL vs. Hive QL
B. HBase
1. Introduction of HBase
2. Characteristics of HBase
3. HBase Architecture
4. HBase Vs RDBMS
5. HBase Shell Commands
Workshop Course Contents
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Big Data & Hadoop Project to be covered
Word Count for a large amount of data With Using (MapReduce,Pig)
Temperature Sensor Conversation With Using Pig DATA Sorting With Large data Set With Hive &
HBase Write Complex MapReduce Program