This document discusses cloud computing, big data, Hadoop, and data analytics. It begins with an introduction to cloud computing, explaining its benefits like scalability, reliability, and low costs. It then covers big data concepts like the 3 Vs (volume, variety, velocity), Hadoop for processing large datasets, and MapReduce as a programming model. The document also discusses data analytics, describing different types like descriptive, diagnostic, predictive, and prescriptive analytics. It emphasizes that insights from analyzing big data are more valuable than raw data. Finally, it concludes that cloud computing can enhance business efficiency by enabling flexible access to computing resources for tasks like big data analytics.

1. Mr. Kailash Shaw [ HOD ( CSE DEPT.) ]
Mrinal Kumar - 1301292599
Pranav Kumar - 1301292603
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2.  Introduction
 Why Cloud Computing
 Benefits of Cloud Computing
 Characteristics
 Advantages of Cloud Computing
 Disadvantages of Cloud
Computing
 How Cloud Computing Works
 Challenges of Cloud Computing
 Layers of Cloud Computing
 Components of Cloud Computing
 Big Data
 3 Vs of Big Data
 Importance of Big Data
 What Comes Under Big Data
 Hadoop
 Hadoop Architecture
 Hadoop With Big Data
 Map Reduce
 Why Data Analytics
 Types of Analysis
 Types of Data Analytics
 Big Data Analytics
 Conclusion
 References
 Thanking You
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3. Cloud computing is an internet based computer
technology. It is the next stage technology that
uses the clouds to provide the services
whenever and wherever the user need it. It
provides a method to access several servers
world wide.
What is Cloud?
A cloud is a combination of networks,
hardware, services, storage, and interfaces
that helps in delivering computing as a
service.
What is Cloud Computing ?
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4. Why Cloud Computing?
Without Cloud Computing With Cloud Computing
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5. Benefits of Cloud Computing
 Cloud computing enables companies and
applications, which are system
infrastructure dependent, to be
infrastructure-less.
 By using the Cloud infrastructure on “pay
as used and on demand”, all of us can save
in capital and operational investment!
 Clients can:-
 Put their data on the platform instead of on their
own desktop PCs and/or on their own servers.
 They can put their applications on the cloud and
use the servers within the cloud to do processing
and data manipulations etc.
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6. Agile
Highly Reliable
Independent of Device
and Location
Low Cost
Pay-Per-Use
Easy to Maintain
Highly Scalable
Multi-Shared
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7. Advantages of Cloud Computing
 Lower cost computer users
 Lower IT infrastructure
 Fewer Maintenance cost
 Lower Software Cost
 Instant Software updates
 Increased Computing Powers
 Unlimited storage capacity
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8. Disadvantages of Cloud Computing
 Requires a constant Internet
connection
 Stored data might not be secured
 Limited control and flexibility
 More risk on information leakage
 Users cannot be aware of the
network
 Dependencies on service suppliers for
implementing data management
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10.  Use of cloud computing means dependence on
others and that could possibly limit flexibility
and innovation
 Security could prove to be a big issue:
 It is still unclear how safe out-sourced data is and when using these services
 Ownership of data is not always clear.
 Data Centre can become environmental
hazards: Green Cloud
 Cloud Interoperability is still an issue.

11. Layers of Cloud Computing
 Infrastructure as a service (IaaS):-It provides cloud infrastructure
in terms of hardware as like memory, processor, speed etc.
 Platform as a service (PaaS):It provides cloud application
platform for the developer.
 Software as a service (SaaS)::It provides the cloud applications
to users directly without installing anything on the system.
These applications remains on cloud.

12. Components Of Cloud Computing

13. Big Data
Big Data refers to a collection of data sets so large
and complex. It is impossible to process them with
the usual databases and tools because of its size and
associated numbers. Big data is hard to capture, store,
search, share, analyze and visualize.

14. 3 Vs of Big Data
 The “BIG” in big data isn’t just about volume
 Volume
 Variety
 Velocity

15. Importance of Big Data
The importance of big data does not revolve around how much data you have ,
but what you do with it.
You can take data from any source and analyze it to find answer that enables,
 Cost reductions.
 Time reductions.
 New product development and optimized offerings .
 Smart decision making.

16.  Black Box Data
 Social Media Data
 Stock Exchange Data
 Power Grid Data
 Transport Data
 Search Engine Data
 Structured data
 Semi Structured data
 Unstructured data

17. What is Hadoop ?
 Hadoop is an open-source software framework for storing
data and running applications on clusters of commodity
hardware. It provides massive storage for any kind of
data, enormous processing power and the ability to handle
virtually limitless concurrent tasks or jobs.
 The software framework that supports HDFS,
MapReduce and other related entities is called the project
Hadoop or simply Hadoop.
 This is open source and distributed by Apache.

18. Hadoop Ecosystem
Apache Oozie (Workflow)
Pig Latin
Data Analysis
Mahout
Machine Learning
HDFS (Hadoop Distributed File System)
Map Reduce Framework
Flume Sqoop
Unstructured or
Semi-Structured data
Structured data
Pig Latin
Data Analysis
Mahout
Machine Learning
H Base
Hive
DW System

19. With Big Data
Hadoop is the core platform for
structuring Big Data, and solves the
problem of formatting it for
subsequent analytics
purposes. Hadoop uses a distributed
computing architecture consisting of
multiple servers using commodity
hardware, making it relatively

20. Cost Effective System
Large Cluster of Notes
Parallel Processing
Distributive Data
Automatic failover management
Data Locality optimization
Heterogeneous Cluster
Scalability

21. Map Reduce
MapReduce is a programming model that Google has used
successfully in processing its “big-data” sets (~ 20000 peta bytes
per day)
 A map function extracts some intelligence from
raw data.
 A reduce function aggregates according to some
guides the data output by the map.
 Users specify the computation in terms of a map
and a reduce function,
 Underlying runtime system automatically
parallelizes the computation across large-scale
clusters of machines, and
 Underlying system also handles machine failures,
efficient communications, and performance issues.

22. Broken into pieces
[ MAP ]
Computation
Computation
Computation
Computation
Computation
Computation
Shuffle and Sort

23. Why Data Analysis?
It is important to remember that the primary
value from big data does not come from the
data in its raw form but from the processing
and analysis of it and the insights, products
and services that emerge from analysis.

24. For unstructured data to be useful it must be analysed to extract and
expose the information it contains
Different types of analysis are possible, such as:-
 Entity analysis – people, organisations, objects and events, and the relationships
between them
 Topic analysis – topics or themes, and their relative importance
 Sentiment analysis – subjective view of a person to a particular topic
 Feature analysis – Inherent characteristics that are significant for a particular analytical
perspective (e.g. land coverage in satellite imagery)
Types Of Analysis

25. Types Of Data Analytics
Analytic Excellence leads to better decisions:-
 Descriptive Analytics : What is happening?
 Diagnostic Analytics : Why did it happen?
 Predictive Analytics : What is likely going to
happen?
 Prescriptive Analytics : What should we do about it?

26. Analytics
 Focus On :-
 Predictive Analysis
 Data Science
 Data Sets:-
 Large Scale Data Sets
 More type of Data
 Raw Data
 Complex Data Models
 Supports:-
 Correlations – new insight more accurate answer

27.  Two IT initiatives are currently top of mind for organizations across the globe i.e.
 Big Data Analytics
 Cloud Computing
 As a delivery model for IT services , cloud computing has the potential to enhance
business agility and productivity while enabling greater efficiencies and reducing
costs.
 In the current scenario , Big Data is a big challenge for the organizations .
To store and process such large volume of data , variety of data and velocity of data
Hadoop came into existence.
 Our presentation is all about Cloud Computing , Big Data & Big Data Analytics.

28. www.slideshare.com/cloud&bigdata
www.hadooptutorial.com
www.javatpoint.com/cloudcomputing
www.ibm.com/ibm/academy