Social Analytics via Hadoop

© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com
Web and Social Media
Analytics using Hadoop

Session Objectives
ᗍ Introduction to Big Data and Hadoop
ᗍ Understanding HDFS
ᗍ Introduction to MapReduce
ᗍ Social & Web Analytics via Hadoop
ᗍ BIG Data & Hadoop Course Syllabus
ᗍ Webinar by Skillspeed
Get Started with BIG Data & Hadoop

Big Data and its Challenges

Big Data and its Challenges
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management
tools or traditional data processing applications
Systems / Enterprises generate huge amount of
data from Terabytes to and even Petabytes of
information
It’s very difficult to manage such huge data……

Who Generates Big Data?
Have you ever wondered how Google, Facebook or LinkedIn manages to store and utilize the huge data?
Today, it is becoming a problem for all of us to manage such BIG DATA…. Get Started with BIG Data & Hadoop

Hadoop can be used for easy processing of such huge Data…..
We will answer how?
Before that let’s understand what is Hadoop?

Hadoop and its Characteristics
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model
It is an Open-source Data Management technology with scale-out storage and distributed processing
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop

Hadoop Ecosystem
Flume Sqoop
Import Or Export
Unstructured or
Semi-Structured data Structured Data
Apache Oozie (Workflow)
HDFS
(Hadoop Distributed File System)
Pig Latin
Data Analysis
Hive
DW System
MapReduce Framework HBase
Other
YARN
Frameworks (MPI,
GIRAPH)
YARN
Cluster Resource Management

Hive Architecture
Driver
(Compiler, Optimizer, Executor)
Thrift Server
Web
Interface
Command Line Interface
Metastore
JDBC ODBC
HIVE
HADOOP
(MapReduce + HDFS)
Job Tracker NameNode
Data Node
+
Task Tracker

Querying
select sum(mt.Trade_Currency_Value),mt.Transaction_Date,mt.Office_ID from share_trans4
mt group by mt.Transaction_Date,mt.Office_ID order by Transaction_Date asc
Syntax
SELECT [ALL | DISTINCT] select_expr, select_expr, ...
FROM table_reference
[WHERE where_condition]
[GROUP BY col_list]
[HAVING having_condition]
[CLUSTER BY col_list | [DISTRIBUTE BY col_list] [SORT BY col_list]]
[LIMIT number]
Example:

HDFS

Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity
machines, providing very high aggregate bandwidth across the cluster
HDFS and its Components
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written
in Java for the Hadoop framework
NameNode
ᗍ Storage side master of the system
ᗍ It maintains, manages, and administers the data blocks present on the DataNodes
DataNodes
ᗍ Slave machines which provide the actual and redundant storage
ᗍ End points for client read and write operations

HDFS Architecture
NameNode
Client
Rack 1 Client Rack 2
Metadata (Name, replicas,...):
/home/foo/data, 3,…
Read DataNodes
Write
Replication
Blocks
Block ops
DataNodes
Metadata ops

HDFS NameNode
Keeps Meta data in Main Memory
ᗍ The entire metadata is in main memory
ᗍ FS meta-data is not loaded from hard disk
Metadata type
ᗍ Files in HDFS
ᗍ Data Blocks for each file
ᗍ DataNodes for each block
ᗍ File attributes, e.g. access time, replication factor, access control

Secondary NameNode
Secondary NameNode:
ᗍ In HDFS 1.0, not a hot standby for the NameNode
ᗍ By Default connects to NameNode every hour*
ᗍ Housekeeping, backup of NameNode metadata
ᗍ Saved metadata is used to bring up the secondary
NameNode
NameNode
Secondary
NameNode
Metadata
I’’ll take metadata
every hour and
will make it secure

Map Reduce

Map Reduce – Scenario
Let us consider a real life scenario to understand the importance of “Map Reduce” in Hadoop
Suppose, you are the
handling a project which has
x tasks and takes 100 hours
for one resource to complete
1 x 100 = 100 hours
100/10(resources) = 10 hours

Similarly,
= 100 hours 100/10 = 10 hours
Map Reduce – Scenario

More Scenarios on Map-Reduce
Problem Statement:
Find maximum stock market levels recorded in a span of 5 years
Problem Statement:
De-identify personal identifier information

Traditional Solution
matchesSplit Data
Very
Big
Data
All
matches
grep
grep
grep
cat
grep
:
matches
matches
matches
Split Data
Split Data
Split Data

MapReduce Solution
Very
Big
Input
Split Data
All
matches
:
Split Data
Split Data
Split Data
M
A
P
R
E
D
U
C
E
MapReduce Framework

MapReduce Advantages
Two biggest advantages:
ᗍ Takes processing to the data
ᗍ Allows processing data in parallel
a b
c
Map Task
HDFS Block
Data Center
Rack
Node

MapReduce Flow
1. Input data is present in data nodes
2. Map tasks = Input Splits
3. Mappers produce intermediate data
4. Data exchanged among nodes in “shuffling”
5. All data of same key goes to same reducer
6. Reducer output stored at output location
Node 1
INPUT DATA
Map
Node 2
Map
Node 1
Reduce
Node 1
Reduce

What is Expected?
In this section, we will discuss the questions on HDFS and MapReduce that is asked during the interview
This will help you analyze the importance of the topics under study!

What is the use of Namenode in HDFS?
What is DataNode in HDFS?
What is Job Tracker in HDFS?
What is MapReduce?
How does an Hadoop application look like on their basic components?
And many more…………….
The Top 5 Interview Questions

Job Trends – Hadoop

Why SkillSpeed?
Course
Curriculum
from Industry
Experts
Instructor Led
Live Virtual
Sessions
Lifetime access
to Course
Content via
LMS
100% Placement
Assistance
24x7 Support

Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and
Data Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive
Concepts
Module 8
Extending Hive and
HBase Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up
Discussion

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Image References
Google images – credit for google, Facebook and LinkedIn LOGO and Snapshots
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http://www.gopixpic.com/16/time-clock-icon-png-download
http://blog.smartbear.com/requirements/how-to-interview-users-to-find-out-what-they-really-want/
http://www.lincs.fr/research/areas/big-data/
http://www.counsellingpages.co.uk/
http://langfordsconsultancy.com/langfords-training-support-package/
http://cbsepathshala.blogspot.in/2012/05/physics-class-x-chapter-electricity.html
http://mmatycoon.com/tycoontimes/tycoontimesstory.php?SID=1010

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