Big Data Processing with Hadoop : A Review

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 448
Big Data Processing with Hadoop : A Review
Gayathri Ravichandran
Student, Department of Computer Science , M.S Ramaiah Institute of Technology, Bangalore, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – We live in an era where data is being generated
by everything around us. The rate of data generation is so
alarming, that it has engendered apressing needtoimplement
easy and cost-effectivedatastorageandretrievalmechanisms.
Furthermore, big data needs to be analyzed for insights and
attribute relationships, which can lead to better decision-
making and efficient business strategies. In this paper, we will
describe a formal definition of Big Data and look into its
industrial applications. Further, we will understand how
traditional mechanisms proveinadequatefor dataprocessing
due to the sheer volume, velocity and variety of big data. We
will then look into the Hadoop Architecture and its underlying
functionalities. This will include delineations on the HDFSand
MapReduce Framework. We will then review the Hadoop
Ecosystem, and explain each component in detail.
Key Words: Big Data, Hadoop, MapReduce, Hadoop
Components, HDFS
1. INTRODUCTION
1.1 Big Data: Definition
Big data is a collection of large datasets- structured,
unstructured or semi-structured that is being generated
from multiple sources at an alarming rate. Key enablers for
the growth of big data are – increasing storage capacities,
increasing processing power and availability of data. It is
thus important to develop mechanisms for easy storage and
retrieval. Some of the fields that come under the umbrella of
big data are - stock exchange data ( includes buying and
selling decisions), social media data (Facebook andTwitter),
power grid data ( contains information about the power
consumed by each node in a power station) and search
engine data ( Google). Structured data may include
relational databases like MySQL. Unstructured data may
include text files in .doc, .pdf formats as well as media files.
1.2 Benefits of Big Data
Analysis of big data helps in improving business trends,
finding innovative solutions, customer profiling and in
sentimental analysis. It also helps in identifying the root
causes for failures and re-evaluating risk portfolios. In
addition, it also personalizes customer and interaction.
1. Valuable Insights
Valuable insights can be derived from big datasets by
employing proper tools and methodologies. This data
includes those stored in the company database, or those
obtained from social media and other third party sources.
When data is processed and analyzed,onecandrawvaluable
relationships between various attributes that can improve
the quality of decision making. Statistics and industrial
knowledge can be combined to obtain useful insights
2. New Products and Services
Analyzing big data helps theorganizationtounderstandhow
customers perceive their products and services. This aids in
developing new products that are concurrentwithcustomer
needs and demands. In addition, it also facilitates re-
developing of currently existing products to suit customer
requirements.
3. Smart cities
Population increase begets demand. To help cities deal with
the consequences of rapid expansion, big data is being used
for the benefit of the citizens and the environment. For
example, the city of Portland, Oregon adopted a mechanism
for optimizing traffic signals in response to high congestion.
This not only reduced traffic jams in the city, but was also
significant in eliminating 157,000 metric tons of carbon
dioxide emissions.
4. Risk Analysis
Risk is defined as the probability of injury or loss. Risk
management is a very crucial process which is often over-
looked. Frequent analysis of the data will help mitigate
potential risks. Predictive analysis aids the organization to
keep up to date with recent technologies, services and
products. It also identifies the risks involved, and how they
can be mitigated.
5. Miscellaneous
Big data also aids Media, Government, Technology,Scientific
Research and Healthcare in making crucial decisions and
predictions. For example, Google Flu Trends (GFT)provided
estimates of influenza activity for more than 25 countries. It
made accurate predictions about flu activity.
1.3 Challenges of Big Data
1. Volume
Data is being generated at an alarming rate. The sheer
volume of data being generated makes the issue of data

processing a complicated task. Organizations collect and
generate data from a variety of sources and with the help of
technologies such as Hadoop, storage and retrieval of data
has become easier.
2. Velocity
Velocity refers to the rate at which data is being processed.
Sometimes, data may arrive at unprecedented speeds, and
thus it must be dealt with in a timely manner. It should be
processed in such a speed that is compatible for real time
applications.
3. Variety
Data is being generated from various sources , including
social media data, stock exchange data and black box data.
Furthermore, the data canassumevariousforms – numerals,
text, media files, etc. Thus, big data processing mechanisms
must know how to deal with eclectic data.
4. Variability
Data flow can be inconsistent which can be challenging to
manage.
5. Complexity
The relationships between various attributes in a dataset,
hierarchies and data linkages add to the complexity of data
2. LIMITATIONS OF TRADITIONAL APPROACH
The traditional approach consists of a computertostoreand
process big data. Data is stored in a Relational Database like
MySQL and Oracle. This approach works well when the
volume of data is less. However, when dealing with larger
volumes of data, it becomes tedious to process it through a
database server. Hence, this calls for a more sophisticated
approach. We will now look into Hadoop – its modules,
framework and ecosystem.
3. HADOOP
Apache Hadoop is an open source software framework for
storing and processing large clusters of data. It has extensive
processing power and it consists of large networks of
computer clusters. Hadoop makes it possible to handle
thousands of terabytes of data. Hardware failures are
automatically handled by the framework.
Apache Hadoop consists of 4 modules:
a. Hadoop Distributed File System(HDFS)
b. Hadoop MapReduce
c. Hadoop YARN
d. Hadoop Common
This paper will primarily concentrate on the former two
modules.
3.1 Hadoop Distributed File System (HDFS)
Apache Hadoop uses the HadoopDistributedFileSystem.Itis
highly fault tolerant and uses minimal cost hardware. It
consists of a cluster of machines, and files are stored across
them. It also provides file permissions and authentication,
and streaming access to system data.
The following figure depictsthegeneralarchitectureofHDFS
Figure 1: HDFS Architecture
HDFS follows the Master- Slave Architecture. It has the
following components.
1. Name node
The HDFS consists of a single name node, which acts as the
master node. It controls and manages the file system
namespace. A file system namespace consists of a hierarchy
of files and directories, where users can create, remove or
move files based on their privilege. A file is split into one or
more blocks and each block is stored in a Data node. HDFS
consists of more than one Data Nodes.
The roles of the name node are as follows:
a. Mapping blocks to their data nodes.
b. Managing of file system namespace
c. Executing file system operations- opening, closing
and renaming of files.
2. Data node
The HDFS consists of more than one data node. The data
nodes store the file blocks that are mapped onto it by the
Name node. The data nodes are responsible for performing
read and write operations from file systems as per client

request. They also perform block creation and replication.
The minimum amount of data that the system can write or
read is called a block. This value however is not fixed, and it
can be increased.
3.2 Hadoop MapReduce Framework
Hadoop uses the MapReduce framework for
distributed computing applications to process large
amounts of data.Itisadistributedprogrammingmodel
based on the Java Programming language. The data
processing frameworks are called mappers and
reducers. The MapReduce framework is attractive due
to its scalability.
It consists of two important tasks : Map and Reduce
Figure 2: MapReduce Framework
1. Map stage
The map function takes in a set of data as the input, and
returns a key-value pair as the output. The input may be in
the form of a file or directory. The output of the map stage
serves as input to the reduce stage.
2. Reduce stage
The reduce function will combine the data tuples into a
smaller set. The map task always precedes the reduce task.
The output of reduce stage is stored in the HDFS.
3.3 Hadoop Ecosystem
Figure 3: Hadoop Ecosystem
1. HBase
HBASE or Hadoop Database is a NoSQL Database, i.e., it is
non-relational. It is built on top of the HDFS System written
in Java. It is the underlying technology of social media
websites like Facebook.
2. Hive
Hive is a structured Query Language. It uses the Hive Query
Language (HQL), and it deals with structured data. It runs
MapReduce Algorithm as its backend, and it is a data
warehousing framework.
3. Pig
Pig also deals with structured data, and it uses the Pig Latin
Language. It consists of a series of operations applied to
input data, and it uses MapReduce in the back-end. It adds a
level of abstraction to data processing.
4. Mahout
It is an open source Apache MachineLearninglibraryinJava.
It has modules for clustering, categorization, collective
filtering and mining of frequent patterns.

4. CONCLUSION
This paper starts off by giving a formaldefinitiontoBig
Data. Then, the challenges of handling big data are
examined, followed by the limitations of using the
traditional big data processing approach. We then
delve into the details of Hadoop and its components,
and its MapReduce framework.
REFERENCES
[1] Dean, J. and Ghemawat, S., “MapReduce: a flexible data
processing tool” Young, The Technical Writer’s
Handbook. Mill Valley, CA: University Science, 1989.
[2] Varsha B.Bobade, “Survey Paper on Big Data and
Hadoop”, IRJET, Volume 3, Issue 1, January 2016
[3] Bijesh Dhyani,Anurag Barthwal, “Big Data Analytics
using Hadoop”, International Journal of Computing
Applications, Volume 108, No.12, December 2014
[4] Ms. Gurpreet Kaur,Ms.ManpreetKaur, “ReviewPaperon
Big Data using Hadoop”, International Journal of
Computing Engineering and Technology, Volume 6,
Issue 12, Dec 2015, pp. 65-71
[5] Harshwardhan S. Bhosale et al, “Review paper on Big
Data using Hadoop”, International Journal of Scientific
and Research Publications, Volume 4, Issue 10, October
2014
[6] Poonam S. Patil et al. “Survey Paper on Big Data
Processing and Hadoop Components”, International
Journal of Science and Research, Volume 3, Issue 10,
October 2014.
[7] Apache HBase. Available at http://hbase.apache.org
[8] Apache Hive. Available at http://hive.apache.org
[9] Abhishek S, “Big Data and Hadoop”, White Paper
[10] Konstantin Shvachko et.al, “The HadoopDistributedFile
System”

Big Data Processing with Hadoop : A Review

More Related Content

What's hot (18)

Similar to Big Data Processing with Hadoop : A Review (20)

More from IRJET Journal (20)

Recently uploaded (20)

Big Data Processing with Hadoop : A Review