Cloud Computing & Big Data

Gandhi Engineering College

Seminar On

Big Data Analytics with Cloud Submitted To:-

Mr. Kailash Shaw [ HOD ( CSE DEPT.) ]

Submitted By:-Mrinal Kumar - 1301292599Pranav Kumar - 1301292603

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Contents 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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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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Why Cloud Computing?

Without Cloud Computing With Cloud Computing

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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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Characteristics

Agile

Highly Reliable

Independent of Device and Location

Low Cost

Pay-Per-Use

Easy to Maintain

Highly Scalable

Multi-Shared

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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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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 management8

Challenges of cloud computing 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.

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.

Components Of Cloud Computing

Big Data

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

3 Vs of Big Data

The “BIG” in big data isn’t just about volume

Volume

Variety

Velocity

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.

What Comes Under Big Data? Black Box Data 

Social Media Data

Stock Exchange Data

Power Grid Data

Transport Data

Search Engine Data

Structured data

Semi Structured data

Unstructured data

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.

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 LatinData Analysis

MahoutMachine Learning

H Base

HiveDW System

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 inexpensive to scale and support extremely large data stores.

Features of HadoopCost Effective SystemLarge Cluster of NotesParallel ProcessingDistributive DataAutomatic failover managementData Locality optimizationHeterogeneous Cluster Scalability

Map ReduceMapReduce 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.

Map Reduce

Big Data

Broken into pieces [ MAP ]

Computation

Computation

Computation

Computation

Computation

Computation

Shuffle and Sort

ReduceBig Data ReduceBig Data Reduce

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.

For unstructured data to be useful it must be analysed to extract and expose the information it containsDifferent 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

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?

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

Conclusion 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.

References

www.slideshare.com/cloud&bigdata

www.hadooptutorial.com

www.javatpoint.com/cloudcomputing

www.ibm.com/ibm/academy

Any Queries