...Hadoop Distribution Comparison Tiange Chen The three kinds of Hadoop distributions that will be discussed today are: Apache Hadoop, MapR, and Cloudera. All of them have the same goals of performance, scalability, reliability, and availability. Furthermore, all of them have advantages including massive storage, great computing power, flexibility (Store and process data whenever you want, instead of preprocess before storing data like traditional relational databases. And it enables users to easily access new data sources including social media, email conversations, etc..), fault tolerance (One node fails, jobs still works on other nodes because data is replicated to other nodes in the beginning, so the computing does not fail), low cost (Use commodity hardware to store data), and scalability (More nodes, more storage, and little administration.). Apache Hadoop is the standard Hadoop distribution. It is open source project, created and maintained by developers from all around the world. Public access allows many people to test it, and problems can be noticed and fixed quickly, so their quality is reliable and satisfied. (Moccio, Grim, 2012) The core components are Hadoop Distribution File System (HDFS) as storage part and MapReduce as processing part. HDFS is a simple and robust coherency model. It is able to store large amount of information and provides steaming read performance. However, it is not strong enough in the aspect of easy management and seamless integration...
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...International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013 ISSN 2250-3153 1 Big Data Landscape Shubham Sharma Banking Product Development Division, Oracle Financial Services Software Ltd. Bachelor of Technology Information Technology, Maharishi Markandeshwar Engineering College Abstract- “Big Data” has become a major source of innovation across enterprises of all sizes .Data is being produced at an ever increasing rate. This growth in data production is driven by increased use of media, fast developing organizations, proliferation of web and systems connected to it. Having a lot of data is one thing, being able to store it, analyze it and visualize it in real time environment is a whole different ball game. New technologies are accumulating more data than ever; therefore many organizations are looking forward to optimal ways to make better use of their data. In a broader sense, organizations analyzing big data need to view data management, analysis, and decision-making in terms of “industrialized” flows and processes rather than discrete stocks of data or events. To handle these aspects of large quantities of data various open platforms had been developed. Index Terms- Big Technologies,Tools Data, Landscape,Open Platforms, nearly 500 exabytes per day .To put the numbers in perspective this is equivalent to 5×1020 bytes per day. Almost 200 times higher than all the sources combined together in the world. To handle this huge chunk of...
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...translated to Map Reduce Program during execution HIVE : Provides adhoc SQL like queries for data aggregation and summarization Written by JEFF from FACEBOOK. Database on top of Hadoop HiveQL is the query language. Runs like SQL with less features of SQL HBASE: Database on top of Hadoop. Real-time distributed database on the top of HDFS It is based on Google’s BIG TABLE – Distributed non-RDBMS which can store billions of rows and columns in single table across multiple servers Handy to write output from MAP REDUCE to HBASE ZOO KEEPER: Maintains the order of all animals in Hadoop.Created by Yahoo. Helps to run distributed application and maintain them in Hadoop. SQOOP: Sqoops the data from RDBMS to Hadoop. Created by Cloudera API to extract data from external databases Pulls data from Hadoop and place it in HIVE Put RDBMS data to HDFS Flume and scoop for distributed reading of large data MAHOUT: Machine learning...
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...Running Head: BUSINESS INTELLIGENCE A REVIEW OF LITERATURE Business Intelligence: A Review of Literature Richard Clark Liberty University Abstract An explorative and descriptive review of literature on Business Intelligence (BI). It presents various definitions of the term “Business Intelligence” and determines that regardless the definition is a “means” to “end”, which sound business or organizational decision making, specifically in strategic planning and management. It also presents several important differentiation of Business Intelligence from data-centric technologies, and enterprise applications. It describes business intelligence architecture and its components. It identifies capabilities and benefits to be derive from it, barriers to its successful implementation, and critical success factors. It identifies BI software vendors, trends and forecast, and ethical considerations. It concludes with a conceptual framework that shows business intelligence as a process that produces intelligence necessary for strategic planning and management, that in turn results to a strategy that creates competitive advantages. This paper is an explorative and descriptive review of literature on Business Intelligence (BI). An exploratory research as the name suggests explores a problem or a situation with the purpose of defining an ambiguous problem. It is undertaken from the perspective of a student in management and of an IT “dummy”. Consequently, the review strategy is...
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...Professionals John King & Roger Magoulas Take the Data Science Salary and Tools Survey As data analysts and engineers—as professionals who like nothing better than petabytes of rich data—we find ourselves in a strange spot: We know very little about ourselves. But that’s changing. This salary and tools survey is the second in an annual series. To keep the insights flowing, we need one thing: People like you to take the survey. Anonymous and secure, the survey will continue to provide insight into the demographics, work environments, tools, and compensation of practitioners in our field. We hope you’ll consider it a civic service. We hope you’ll participate today. Make Data Work strataconf.com Presented by O’Reilly and Cloudera, Strata + Hadoop World is where cutting-edge data science and new business fundamentals intersect— and merge. n n n Learn business applications of data technologies Develop new skills through trainings and in-depth tutorials Connect with an international community of thousands who work with data Job # 15420 2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King and Roger Magoulas 2014 Data Science Salary Survey by John King and Roger Magoulas The authors gratefully acknowledge the contribution of Owen S. Robbins and Benchmark Research Technologies, Inc., who conducted the original 2012/2013 Data Science Salary Survey referenced in the article...
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...* Why Big Data analytics is important? Big data is a term that refers to data sets or combinations of data sets whose size (volume), complexity (variability), and rate of growth (velocity) make them difficult to be captured, managed, processed by conventional technologies and tools, within certain time period to make them useful. Big data is vital in fact that when huge information is successfully and effectively caught, prepared organizations can pick up a more finish comprehension of their business, clients, items, contenders, and so on. This can prompt effectiveness enhancements, expanded deals, lower costs, better client benefit, or enhanced items and administrations. Following are some of the examples of big data in different fields: Utilizing information technology (IT) logs to enhance IT investigating and security rupture discovery, pace, viability, and future event avoidance. Use of voluminous call focus data all the more rapidly, keeps in mind the end goal to enhance client association and fulfilment. Use of online networking content keeping in mind the end goal to better and more rapidly client feeling about you/your clients, and enhance items, administrations, and client association. Fraud detection and prevention in any industry that procedures budgetary exchanges on-line, for example, shopping, keeping money, contributing, protection and medicinal services claims. Use of money related business sector exchange data to all the more rapidly evaluate...
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...------------------------------------------------- BIG Data February 8, 2015 Srinivas gogineni SAI SRAVAN KOLUKULA February 8, 2015 Srinivas gogineni SAI SRAVAN KOLUKULA Introduction Big data burst upon the scene in the first decade of the 21st century. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning. Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings. Davenport.T (2013). Big Data is emerging from the realms of science projects at Web companies to help companies like telecommunication giants understand exactly which customers are unhappy with service and what processes caused the dissatisfaction, and predict which customers are going to change carriers. To obtain this information, billions of loosely-structured bytes of data in different locations needs to be processed until the needle in the haystack is found. The analysis enables executive management to fix faulty processes or people and maybe be able to reach out to retain the at-risk customers. The real business impact is that big data technologies can do this in weeks or months, four-or-more-times faster than traditional data warehousing approaches. Floyer.D (2015). Literature Review The IT techniques and tools to execute big data processing...
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...International Journal of Computers & Technology Volume 1 No.1 Dec. 2011 Comparative Analysis of Various Cloud Technologies Dr. Gurdev Singh Professor and Head Department of Computer science & Engineering Adesh Institute of Engineering & Technology Faridkot (India) Akanksha Lecturer Department of Computer science & Engineering Adesh Institute of Engineering & Technology Faridkot (India) singh.gndu@gmail.com 1. ABSTRACT With the increasing prevalence and demand of large scale cloud computing environment, a researcher has to draw more attention towards the services provided by the CLOUD. As the access to the server is increasing, centralized and distributed computing architecture will produce bottlenecks data which affect the quality of cloud computing services and bring the huge support to users. In this paper we are going to propose certain vital aspects such as memory utilization, storage capacity to check the efficiency and performance of various clouds in cloud computing environment. This is based upon the static data. The proposed mechanism enables users to access memories in various systems depending on the predefined criteria. Selection method for accessing the memory of a resource is properly introduced in this paper. Our evaluation results show that the aggregation of various clouds is effective in indicating the better efficiency and also to reduce network traffic sent over cloud networks. er.akanksha1988@gmail.com WAN networking. The interlinking and...
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...BUS211f(2) ANALYZING BIG DATA I1 Spring 2014—MW 8:00–9:20 am Location: Sachar 116 (International Hall) Prof. Bharatendra Rai 313-282-8309 (mobile) brai@brandeis.edu Office: Sachar 1C Hours: MW, 9:30 – 10:15 and by appointment TA: TBD This is a two credit module that examines the opportunities and industry disruption in an era of massive, high velocity, unstructured data and new developments in data analytic. We treat some strategic, ethical, and technical dimensions of big data. The technical foci of the course include data structures, data warehousing, Structured Query Language (SQL), and high-impact visual displays. The principal objective of the course is to help students build understanding of data as an essential competitive resource, and acquire advanced computer skills through cases and hands-on applications. Assignments and classroom time will be devoted to both to analysis of current developments in analytics and to gaining experience with current tools. Davenport , Thomas H. and Harris, Jeanne G. Competing on Analytics: The New Science of Winning. Cambridge: Harvard Business School Press, 2007. ISBN 978-1422103326. Available for purchase at the bookstore. There is a required on-line course pack available for purchase at the Harvard Business Publishing website at this URL: https://cb.hbsp.harvard.edu/cbmp/access/23455671 This link is also available on LATTE . See last page of Syllabus for course pack contents. Other readings as posted on LATTE site. Learning Goals...
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...BUS211f(2) ANALYZING BIG DATA I1 Spring 2014—MW 8:00–9:20 am Location: Sachar 116 (International Hall) Prof. Bharatendra Rai 313-282-8309 (mobile) brai@brandeis.edu Office: Sachar 1C Hours: MW, 9:30 – 10:15 and by appointment TA: TBD This is a two credit module that examines the opportunities and industry disruption in an era of massive, high velocity, unstructured data and new developments in data analytic. We treat some strategic, ethical, and technical dimensions of big data. The technical foci of the course include data structures, data warehousing, Structured Query Language (SQL), and high-impact visual displays. The principal objective of the course is to help students build understanding of data as an essential competitive resource, and acquire advanced computer skills through cases and hands-on applications. Assignments and classroom time will be devoted to both to analysis of current developments in analytics and to gaining experience with current tools. Davenport , Thomas H. and Harris, Jeanne G. Competing on Analytics: The New Science of Winning. Cambridge: Harvard Business School Press, 2007. ISBN 978-1422103326. Available for purchase at the bookstore. There is a required on-line course pack available for purchase at the Harvard Business Publishing website at this URL: https://cb.hbsp.harvard.edu/cbmp/access/23455671 This link is also available on LATTE . See last page of Syllabus for course pack contents. Other readings as posted on LATTE site. Learning Goals...
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...4. 4.1 Big Data Introduction In 2004, Wal-Mart claimed to have the largest data warehouse with 500 terabytes storage (equivalent to 50 printed collections of the US Library of Congress). In 2009, eBay storage amounted to eight petabytes (think of 104 years of HD-TV video). Two years later, the Yahoo warehouse totalled 170 petabytes1 (8.5 times of all hard disk drives created in 1995)2. Since the rise of digitisation, enterprises from various verticals have amassed burgeoning amounts of digital data, capturing trillions of bytes of information about their customers, suppliers and operations. Data volume is also growing exponentially due to the explosion of machine-generated data (data records, web-log files, sensor data) and from growing human engagement within the social networks. The growth of data will never stop. According to the 2011 IDC Digital Universe Study, 130 exabytes of data were created and stored in 2005. The amount grew to 1,227 exabytes in 2010 and is projected to grow at 45.2% to 7,910 exabytes in 2015.3 The growth of data constitutes the “Big Data” phenomenon – a technological phenomenon brought about by the rapid rate of data growth and parallel advancements in technology that have given rise to an ecosystem of software and hardware products that are enabling users to analyse this data to produce new and more granular levels of insight. Figure 1: A decade of Digital Universe Growth: Storage in Exabytes Error! Reference source not found.3 1 ...
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...Big Data [Name of Writer] [Name of Institution] Introduction The term Big Data is gaining more followers and popularity. However, despite this trend, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. The term Big Data is gaining more followers and popularity. However, despite this trend so evident, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. There are multiple techniques in terms of huge database storing approaches that can store petabytes, exabytes and may be zetabytes data. These options are Cassendara, Mongodb and HBase. We will discuss about them one by one and in a proper research method and will compare them in order to contrast their difference and efficiency. Research Background One problem in understanding the phenomenon is that the size of these data sets the volume greatly exceeds the Data warehouse. A plane collects 10 terabytes of information from sensors every 30 minutes flight, while the Stock Exchange of New York collects structured information 1 TB per day. In the context of Big Data, volumes are reaching peta bytes, exa bytes and then soon to zeta bytes. For instance, Apple has just announced that 7 trillion send daily notifications to iOS devices. The explosion of information in social networks, blogs, and emails is characterized the presence of data key...
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...Strategic Thinking Assignment Word Count: 1,500 | It is argued that the provision of Massive Open Online Courses (MOOCs) is becoming a new disruptive innovation that will radically change the scene of higher education. This report will analyse the external environment for MOOC provider Udacity, evaluate their competitive strategy and then discuss the future outlook for them and the industry. | Contents Introduction 2 The External Environment 2 Udacity’s competitive strategy 4 The future of the industry and how Udacity can sustain competitive advantage 6 References 7 Appendix 1 – PEST Analysis Table 11 Appendix 2 – PEST Analysis Graph 12 Appendix 3 – Product Life Cycle 13 Appendix 4 – MOOC Industry Life Cycle 14 Appendix 5 – Strategic Groups 1 15 Appendix 6 – Strategic Groups 2 16 Appendix 7 – Porter’s Five Forces (Current) 17 Appendix 8 – Porter’s Five Forces (Future) 18 Appendix 9 – SWOT Analysis 19 Appendix 10 – New Strategies 20 Appendix 11 – Commonly Required Skills and Resources 21 Introduction Udacity is a massive open online course (MOOC) provider based in Silicon Valley, offering career-focused education. They are partnered with tech-giants Google and Facebook and their mission is to “bring accessible, affordable, engaging, and highly effective higher education to the world” (Udacity, 2015). The External Environment Udacity’s macro environment (appendix 1) shows increasing numbers of mature and part-time learners...
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...Building Data Science Teams The Skills, Tools, and Perspectives Behind Great Data Science Groups DJ Patil THE SIMPLEST WAY TO BRING MapReduce and SQL www.asterdata.com Optimized in One Database Appliance Everyone knows data is the new black. The Aster MapReduce Analytics Portfolio enables customers to quickly make use of their data for actionable insights, analysis and product innovation. - Jonathan Goldman, Director of Analytics, Teradata Aster (and former Principal Data Scientist at LinkedIn) Learn More www.Asterdata.com/MapReduce Change the world with data. We’ll show you how. strataconf.com Sep 25 – 27, 2013 Boston, MA Oct 28 – 30, 2013 New York, NY Nov 11 – 13, 2013 London, England ©2013 O’Reilly Media, Inc. O’Reilly logo is a registered trademark of O’Reilly Media, Inc. 13110 Building Data Science Teams DJ Patil Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo Building Data Science Teams by DJ Patil Copyright © 2011 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribookson line.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com. Editor: Mike Loukides Printing History: Many...
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...COMPANY PROFILE Dell Inc. REFERENCE CODE: 8E2C53C7-29AC-4848-9511-9B752758E3B4 PUBLICATION DATE: 24 Sep 2012 www.marketline.com COPYRIGHT MARKETLINE. THIS CONTENT IS A LICENSED PRODUCT AND IS NOT TO BE PHOTOCOPIED OR DISTRIBUTED. Dell Inc. TABLE OF CONTENTS TABLE OF CONTENTS Company Overview..............................................................................................3 Key Facts...............................................................................................................3 Business Description...........................................................................................4 History...................................................................................................................6 Key Employees...................................................................................................11 Key Employee Biographies................................................................................12 Major Products and Services............................................................................20 Revenue Analysis...............................................................................................21 SWOT Analysis...................................................................................................22 Top Competitors.................................................................................................28 Company View........................................................
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