Big Data in Cloud Computing Review and Opportunities- Tutors India

1. BIG DATA IN CLOUD COMPUTING REVIEW ANDOPPORTUNITIES An Academic presentationby Dr. Nancy Agens, Head, Technical Operations,TutorsIndia Group www.tutorsindia.com Email:info@tutorsindia.com

2. Today'sDiscussion Introduction Classification of Big Data CloudComputing Relationship between the Cloud Computing and Big Data CaseStudies Types ofDatabases Conclusion OUTLINE

3. Introduction The rise of big datain daily life is on the rise in almost all domains andapplications. Its combination with cloud computing is a major attraction in ITsector. The other advantage is that both these technologiesare still in their evolutionary stage, where they are getting improvedregularly. A suitable customer may start using Cloud Computing when in need of quick deployment and scaling of theirapplications. Big datacannot be considered as a replacement for relational database systems and big datasolves specific problem statement related to larger datasets. Contd..

4. Figure 1: Characteristics of BigData

5. Classification of BigData TYPE OFANALYSIS DATAFREQUENCY Contd..

6. DATATYPE HARDWARE Contd..

7. DATACONSUMERS

8. CloudComputing Cloud computing delivers computing serviceslike servers, storage, databases, networking, software, analytics and intelligence over the internet for faster innovation, flexible resources, heavy computation, parallel data processing and economies ofscale. The organizations are empowered in order to concentrate more on core business by completely abstracting computation, storage and network resources toworkloads. Contd..

9. Figure 2: Types of CloudComputing Contd..

10. Figure 3: Cloud ComputingServices

11. Relationship between the Cloud Computing and BigData The relationship between big data and cloud computing follows input, processingand output models. The input is the big dataobtained from various data sources such as cellular and other smart devices in either structured, unstructured or semi-structuredformat. This voluminous data is cleaned and then stored using Hadoop or other datastores. The stored data is in turn processed through cloud computing tools and techniquesfor providingservices. Output represents the value obtained after data is being processed for analysisand visualization.

12. CaseStudies TWEET MINING INCLOUD Cloud computing is used to gather and analyzetweets. Amazon cloud infrastructure was used to perform all the computations. Tweets were crawled and later page ranking algorithm wasapplied. Page Ranking is used by Google to define the importance of a web page. Twitter social graph to computePageRank. Contd..

13. REDBUS Redbus is an online travel agency for bus ticket booking inIndia. Redbus decided to use Google data infrastructure for data processing and analysis in order to improve customer sales and management of the ticket bookingsystem. Google BigQuery enabled RedBus to process massive amounts of booking and inventory data withinseconds. Applications that reside on multiple servers continuously streams customer searches, seat inventory and booking information to centralized data collectionsystem.

14. Types ofDatabases Modern databases need to handle large volume and different variety of dataformats. Database architects have produced NoSQL and NewSQL as alternativesto relational database. NoSQL supports structured, semi-structured and unstructureddata. NewSQL is a new approach to relational databases that combines ACID transactions of RDBMSs and horizontal scalability ofNoSQL.

15. Big Data BusinessChallenges UTILITIES: POWER CONSUMPTIONPREDICTION Utility companies use smart meter to measure gas and electricityconsumption. A big datainfrastructure needs to monitor and analyse power generation and consumption using smartmeters. SOCIAL NETWORK: SENTIMENT ANALYSIS Social networking companies such as Twitter needs to determine what users are saying and topic which are trending in order to perform sentimentanalysis. Contd..

16. TELECOMMUNICATION: PREDICTIVEANALYTICS Telecommunication provides need to build churn models which depends on the customer profile dataattributes. Predictive analytics can predict churn by analyzing the subscribers callingpatterns. CUSTOMER SERVICE: CALL MONITOR Call center big data solutions use application logs to improveperformance. The log files needs to be consolidated from different formats before they can be used foranalysis. Contd..

17. BANKING: FRAUDDETECTION Banking companies should be able to prevent fraud on a transaction or a useraccount. Big datasolutions should analyse transactions in real time and provide recommendations for immediate action and stopfraud. RETAILERS: PRODUCT RECOMMENDATION Retailers can monitor user browsing patterns and history of products purchased and provide a solution to recommend products based onit. Retailers need to make privacy disclosures to the users before implementingthese applications.

18. Conclusion In the big data era of innovation and competition driven by advancements in cloud computing has resulted in discovering hidden knowledge from thedata. In this paper we have given an overview of big data applicationsin cloud computing and its challenges in storing, transformation, processing data and some good design principles which could lead to further research.

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