Cloud Computing & Big Data

This presentation is about Cloud Computing & Big Data Analytics.

1. Gandhi Engineering College Seminar On Big Data Analytics with Cloud Submitted To:- Mr. Kailash Shaw [ HOD ( CSE DEPT.) ] Submitted By:- Mrinal Kumar - 1301292599 Pranav Kumar - 1301292603 1

2. 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 2

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 ? 3

4. Why C loud Computing? Without Cloud Computing With Cloud Computing 4

5. Benefits of C loud 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. 5

6. Characteristics Agile Highly Reliable Independent of Device and Location Low Cost Pay-Per-Use Easy to Maintain Highly Scalable Multi-Shared 6

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 7

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 8

9. Challenges o f 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.

10. 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.

11. Components Of Cloud Computing

12. Big Data Big Data refers to a collection of data sets so large a nd complex. It is impossible to process them with t he usual databases and tools because of its size and a ssociated numbers. Big data is hard to capture, store, s earch, share, analyze and visualize.

13. 3 Vs of Big Data The “BIG” in big data isn’t just about volume Volume Variety Velocity

14. 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.

15. 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

16. 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.

17. 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

18. 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.

19. Features of Hadoop Cost Effective System Large Cluster of Notes Parallel Processing Distributive Data Automatic failover management Data Locality optimization Heterogeneous Cluster Scalability

20. 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.

21. Map Reduce Big Data Broken into pieces [ MAP ] Computation Computation Computation Computation Computation Computation Shuffle a nd Sort Reduce Big Data Reduce Big Data Reduce

22. 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 i nsights, products and services that emerge from analysis.

23. 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

24. 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?

25. 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

26. 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.

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

28. Any Queries