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The rise of big data in daily life is on the rise in almost all domains and applications. Its combination with cloud computing is a major attraction in IT sector. While big data deals with large scale data, cloud computing deals with the infrastructure of the data storage. The concerns are simplified when they are used in combination, and are largely effective. The paper explains in detail the various characteristics of Big Data by formulating the ten Vs. The ten characteristics mentioned are velocity, value, volume, variety, variability, validity, veracity, volatility, vulnerability, and visualization. Type of Analysis • Data Frequency • Data Type • Hardware • Data Consumers Cloud computing delivers a computing service like servers, storage, databases, networking, software, analytics and intelligence over the internet for faster innovation, flexible resources, heavy computation, parallel data processing and economies of scale. Click the link to Read the Blog: https://bit.ly/2zkMClQ Contact: Website: www.tutorsindia.com Email: [email protected] United Kingdom: +44-1143520021 India: +91-4448137070 Whatsapp Number: +91-8754446690
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BIG DATA IN CLOUDCOMPUTING REVIEWAND OPPORTUNITIES
An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Tutors India Group www.tutorsindia.comEmail: [email protected]
IntroductionClassification of Big DataCloud ComputingRelationship between the Cloud Computing and Big DataCase StudiesTypes of DatabasesConclusion
OUTLINE
Today's Discussion
The rise of big data in daily life is on the rise in almost all domains and applications.
Its combination with cloud computing is a major attraction in IT sector.
The other advantage is that both these technologies are still in their evolutionary stage,where they are getting improved regularly.
A suitable customer may start using Cloud Computing when in need of quick deploymentand scaling of their applications.
Big data cannot be considered as a replacement for relational database systems andbig data solves specific problem statement related to larger data sets.
Introduction
Contd..
Classification of Big Data
TYPE OF ANALYSIS
DATA FREQUENCY
Contd..
DATA TYPE
HARDWARE
Contd..
DATA CONSUMERS
Cloud computing delivers computing services like servers, storage, databases,networking, software, analytics and intelligence over the internet for fasterinnovation, flexible resources, heavy computation, parallel data processingand economies of scale.
The organizations are empowered in order to concentrate more on corebusiness by completely abstracting computation, storage and networkresources to workloads.
Cloud Computing
Contd..
Figure 2: Types of Cloud ComputingContd..
Figure 3: CloudComputing Services
The relationship between big data and cloud computing follows input, processing andoutput models.
The input is the big data obtained from various data sources such as cellular and othersmart devices in either structured, unstructured or semi-structured format.
This voluminous data is cleaned and then stored using Hadoop or other data stores.
The stored data is in turn processed through cloud computing tools and techniques forproviding services.
Output represents the value obtained after data is being processed for analysis andvisualization.
Relationship between the Cloud Computing and Big Data
TWEET MINING IN CLOUD
Case Studies
Cloud computing is used to gather and analyze tweets.
Amazon cloud infrastructure was used to perform all the computations.
Tweets were crawled and later page ranking algorithm was applied.
Page Ranking is used by Google to define the importance of a web page.
Twitter social graph to compute PageRank.
Contd..
REDBUS
Redbus is an online travel agency for bus ticket booking in India.
Redbus decided to use Google data infrastructure for data processing andanalysis in order to improve customer sales and management of the ticketbooking system.
Google BigQuery enabled RedBus to process massive amounts of bookingand inventory data within seconds.
Applications that reside on multiple servers continuously streams customersearches, seat inventory and booking information to centralized datacollection system.
Modern databases need to handle large volume and different variety of data formats.
Database architects have produced NoSQL and NewSQL as alternatives torelational database.
NoSQL supports structured, semi-structured and unstructured data.
NewSQL is a new approach to relational databases that combines ACIDtransactions of RDBMSs and horizontal scalability of NoSQL.
Types of Databases
Utility companies use smart meter to measure gas and electricity consumption.
A big data infrastructure needs to monitor and analyse power generation andconsumption using smart meters.
Social networking companies such as Twitter needs to determine what users are sayingand topic which are trending in order to perform sentiment analysis.
UTILITIES: POWER CONSUMPTION PREDICTION
SOCIAL NETWORK: SENTIMENT ANALYSIS
Big Data Business Challenges
Contd..
Telecommunication provides need to build churn models which depends on thecustomer profile data attributes.
Predictive analytics can predict churn by analyzing the subscribers calling patterns.
Call center big data solutions use application logs to improve performance.
The log files needs to be consolidated from different formats before they can be usedfor analysis.
TELECOMMUNICATION: PREDICTIVE ANALYTICS
CUSTOMER SERVICE: CALL MONITOR
Contd..
Banking companies should be able to prevent fraud on a transaction or a user account.
Big data solutions should analyse transactions in real time and providerecommendations for immediate action and stop fraud.
Retailers can monitor user browsing patterns and history of products purchased andprovide a solution to recommend products based on it.
Retailers need to make privacy disclosures to the users before implementing theseapplications.
BANKING: FRAUD DETECTION
RETAILERS: PRODUCT RECOMMENDATION
In the big data era of innovation and competition driven by advancements incloud computing has resulted in discovering hidden knowledge from the data.
In this paper we have given an overview of big data applications in cloudcomputing and its challenges in storing, transformation, processing dataand some good design principles which could lead to further research.
Conclusion
