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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