HP and Microsoft Put the Power of Big Data in Everyone's Hands
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Netezza EOL: IBM IAS Is a Flawed Upgrade Path Netezza EOL: IBM IAS Is a Flawed Upgrade Path
Author: Marc Staimer WHITE PAPER Netezza EOL: What You’re Not Being Told About IBM IAS Is a Flawed Upgrade Path Database as a Service (DBaaS) WHITE PAPER • Netezza EOL: IBM IAS Is a Flawed Upgrade Path Netezza EOL: IBM IAS Is a Flawed Upgrade Path Executive Summary It is a common technology vendor fallacy to compare their systems with their competitors by focusing on the features, functions, and specifications they have, but the other guy doesn’t. Vendors ignore the opposite while touting hardware specs that mean little. It doesn’t matter if those features, functions, and specifications provide anything meaningfully empirical to the business applications that rely on the system. It only matters if it demonstrates an advantage on paper. This is called specsmanship. It’s similar to starting an argument with proof points. The specs, features, and functions are proof points that the system can solve specific operational problems. They are the “what” that solves the problem or problems. They mean nothing by themselves. Specsmanship is defined by Wikipedia as: “inappropriate use of specifications or measurement results to establish supposed superiority of one entity over another, generally when no such superiority exists.” It’s the frequent ineffective sales process utilized by IBM. A textbook example of this is IBM’s attempt to move their Netezza users to the IBM Integrated Analytics System (IIAS). IBM is compelling their users to move away from Netezza. In the fall of 2017, IBM announced to the Enzee community (Netezza users) that they can no longer purchase or upgrade PureData System for Analytics (the most recent IBM name for its Netezza appliances), and it will end-of-life all support next year. -
How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population
How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population The author of these comments, Nathan Newman, has been writing for twenty years about the impact of technology on society, including his 2002 book Net Loss: Internet Profits, Private Profits and the Costs to Community, based on doctoral research on rising regional economic inequality in Silicon Valley and the nation and the role of Internet policy in shaping economic opportunity. He has been writing extensively about big data as a research fellow at the New York University Information Law Institute for the last two years, including authoring two law reviews this year on the subject. These comments are adapted with some additional research from one of those law reviews, "The Costs of Lost Privacy: Consumer Harm and Rising Economic Inequality in the Age of Google” published in the William Mitchell Law Review. He has a J.D. from Yale Law School and a Ph.D. from UC-Berkeley’s Sociology Department; Executive Summary: ϋBͤ͢ Dͯ͜͜ό ͫͧͯͪͭͨͮ͜͡ ͮͰͣ͞ ͮ͜ Gͪͪͧ͢͠ ͩ͜͟ Fͪͪͦ͜͞͠͝ ͭ͜͠ ͪͨͤͩ͢͝͠͞ ͪͨͤͩͩͯ͟͜ institutions organizing information not just about the world but about consumers themselves, thereby reshaping a range of markets based on empowering a narrow set of corporate advertisers and others to prey on consumers based on behavioral profiling. While big data can benefit consumers in certain instances, there are a range of new consumer harms to users from its unregulated use by increasingly centralized data platforms. A. These harms start with the individual surveillance of users by employers, financial institutions, the government and other players that these platforms allow, but also extend to ͪͭͨͮ͡ ͪ͡ Ͳͣͯ͜ ͣͮ͜ ͩ͝͠͠ ͧͧ͜͟͞͠ ϋͧͪͭͤͯͣͨͤ͜͢͞ ͫͭͪͤͧͤͩ͢͡ό ͯͣͯ͜ ͧͧͪ͜Ͳ ͪͭͫͪͭͯ͜͞͠ ͤͩͮͯͤͯͰͯͤͪͩͮ to discriminate and exploit consumers as categorical groups. -
EMC Greenplum Data Computing Appliance: High Performance for Data Warehousing and Business Intelligence an Architectural Overview
EMC Greenplum Data Computing Appliance: High Performance for Data Warehousing and Business Intelligence An Architectural Overview EMC Information Infrastructure Solutions Abstract This white paper provides readers with an overall understanding of the EMC® Greenplum® Data Computing Appliance (DCA) architecture and its performance levels. It also describes how the DCA provides a scalable end-to- end data warehouse solution packaged within a manageable, self-contained data warehouse appliance that can be easily integrated into an existing data center. October 2010 Copyright © 2010 EMC Corporation. All rights reserved. EMC believes the information in this publication is accurate as of its publication date. The information is subject to change without notice. THE INFORMATION IN THIS PUBLICATION IS PROVIDED “AS IS.” EMC CORPORATION MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND WITH RESPECT TO THE INFORMATION IN THIS PUBLICATION, AND SPECIFICALLY DISCLAIMS IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Use, copying, and distribution of any EMC software described in this publication requires an applicable software license. For the most up-to-date listing of EMC product names, see EMC Corporation Trademarks on EMC.com All other trademarks used herein are the property of their respective owners. Part number: H8039 EMC Greenplum Data Computing Appliance: High Performance for Data Warehousing and Business Intelligence—An Architectural Overview 2 Table of Contents Executive summary ................................................................................................. -
Issues, Challenges, and Solutions: Big Data Mining
ISSUES , CHALLENGES , AND SOLUTIONS : BIG DATA MINING Jaseena K.U. 1 and Julie M. David 2 1,2 Department of Computer Applications, M.E.S College, Marampally, Aluva, Cochin, India [email protected],[email protected] ABSTRACT Data has become an indispensable part of every economy, industry, organization, business function and individual. Big Data is a term used to identify the datasets that whose size is beyond the ability of typical database software tools to store, manage and analyze. The Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper presents the literature review about the Big data Mining and the issues and challenges with emphasis on the distinguished features of Big Data. It also discusses some methods to deal with big data. KEYWORDS Big data mining, Security, Hadoop, MapReduce 1. INTRODUCTION Data is the collection of values and variables related in some sense and differing in some other sense. In recent years the sizes of databases have increased rapidly. This has lead to a growing interest in the development of tools capable in the automatic extraction of knowledge from data [1]. Data are collected and analyzed to create information suitable for making decisions. Hence data provide a rich resource for knowledge discovery and decision support. A database is an organized collection of data so that it can easily be accessed, managed, and updated. Data mining is the process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amounts of data stored in databases, data warehouses or other information repositories. -
Oracle Big Data SQL Release 4.1
ORACLE DATA SHEET Oracle Big Data SQL Release 4.1 The unprecedented explosion in data that can be made useful to enterprises – from the Internet of Things, to the social streams of global customer bases – has created a tremendous opportunity for businesses. However, with the enormous possibilities of Big Data, there can also be enormous complexity. Integrating Big Data systems to leverage these vast new data resources with existing information estates can be challenging. Valuable data may be stored in a system separate from where the majority of business-critical operations take place. Moreover, accessing this data may require significant investment in re-developing code for analysis and reporting - delaying access to data as well as reducing the ultimate value of the data to the business. Oracle Big Data SQL enables organizations to immediately analyze data across Apache Hadoop, Apache Kafka, NoSQL, object stores and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. KEY FEATURES Rich SQL Processing on All Data • Seamlessly query data across Oracle Oracle Big Data SQL is a data virtualization innovation from Oracle. It is a new Database, Hadoop, object stores, architecture and solution for SQL and other data APIs (such as REST and Node.js) on Kafka and NoSQL sources disparate data sets, seamlessly integrating data in Apache Hadoop, Apache Kafka, • Runs all Oracle SQL queries without modification – preserving application object stores and a number of NoSQL databases with data stored in Oracle Database. -
Microsoft's SQL Server Parallel Data Warehouse Provides
Microsoft’s SQL Server Parallel Data Warehouse Provides High Performance and Great Value Published by: Value Prism Consulting Sponsored by: Microsoft Corporation Publish date: March 2013 Abstract: Data Warehouse appliances may be difficult to compare and contrast, given that the different preconfigured solutions come with a variety of available storage and other specifications. Value Prism Consulting, a management consulting firm, was engaged by Microsoft® Corporation to review several data warehouse solutions, to compare and contrast several offerings from a variety of vendors. The firm compared each appliance based on publicly-available price and specification data, and on a price-to-performance scale, Microsoft’s SQL Server 2012 Parallel Data Warehouse is the most cost-effective appliance. Disclaimer Every organization has unique considerations for economic analysis, and significant business investments should undergo a rigorous economic justification to comprehensively identify the full business impact of those investments. This analysis report is for informational purposes only. VALUE PRISM CONSULTING MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. ©2013 Value Prism Consulting, LLC. All rights reserved. Product names, logos, brands, and other trademarks featured or referred to within this report are the property of their respective trademark holders in the United States and/or other countries. Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this report may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Microsoft Corporation. -
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
Additional praise for Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners “Jared’s book is a great introduction to the area of High Powered Analytics. It will be useful for those who have experience in predictive analytics but who need to become more versed in how technology is changing the capabilities of existing methods and creating new pos- sibilities. It will also be helpful for business executives and IT profes- sionals who’ll need to make the case for building the environments for, and reaping the benefi ts of, the next generation of advanced analytics.” —Jonathan Levine, Senior Director, Consumer Insight Analysis at Marriott International “The ideas that Jared describes are the same ideas that being used by our Kaggle contest winners. This book is a great overview for those who want to learn more and gain a complete understanding of the many facets of data mining, knowledge discovery and extracting value from data.” —Anthony Goldbloom Founder and CEO of Kaggle “The concepts that Jared presents in this book are extremely valuable for the students that I teach and will help them to more fully under- stand the power that can be unlocked when an organization begins to take advantage of its data. The examples and case studies are particu- larly useful for helping students to get a vision for what is possible. Jared’s passion for analytics comes through in his writing, and he has done a great job of making complicated ideas approachable to multiple audiences.” —Tonya Etchison Balan, Ph.D., Professor of Practice, Statistics, Poole College of Management, North Carolina State University Big Data, Data Mining, and Machine Learning Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions. -
Dell Quickstart Data Warehouse Appliance 2000
Dell Quickstart Data Warehouse Appliance 2000 Reference Architecture Dell Quickstart Data Warehouse Appliance 2000 THIS DESIGN GUIDE IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL ERRORS AND TECHNICAL INACCURACIES. THE CONTENT IS PROVIDED AS IS, WITHOUT EXPRESS OR IMPLIED WARRANTIES OF ANY KIND. © 2012 Dell Inc. All rights reserved. Reproduction of this material in any manner whatsoever without the express written permission of Dell Inc. is strictly forbidden. For more information, contact Dell. Dell , the DELL logo, the DELL badge, and PowerEdge are trademarks of Dell Inc . Microsoft , Windows Server , and SQL Server are either trademarks or registered trademarks of Microsoft Corporation in the United States and/or other countries. Intel and Xeon are either trademarks or registered trademarks of Intel Corporation in the United States and/or other countries. Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and names or their products. Dell Inc. disclaims any proprietary interest in trademarks and trade names other than its own. December 2012 Page ii Dell Quickstart Data Warehouse Appliance 2000 Contents Introduction ............................................................................................................ 2 Audience ............................................................................................................. 2 Reference Architecture .............................................................................................. -
Internet of Things Big Data Artificial Intelligence
IGF 2019 Best Practices Forum on Internet of Things Big Data Artificial Intelligence Draft BPF Output Report - November 2019 - Acknowledgements The Best Practice Forum on Internet of Things, Big Data, Artificial Intelligence (BPF IoT, Big Data, AI) is an open multistakeholder group conducted as an intersessional activity of the Internet Governance Forum (IGF). This report is the draft output of the IGF2019 BPF IoT, Big Data, AI and is the product of the collaborative work of many. Facilitators of the BPF IoT, Big Data, AI: Ms. Concettina CASSA, MAG BPF Co-coordinator Mr. Alex Comninos, BPF Co-coordinator Mr. Michael Nelson, BPF Co-coordinator Mr. Wim Degezelle, BPF Consultant (editor) The BPF draft document was developed through open discussions on the BPF mailing list and virtual meetings. We would like to acknowledge participants to these discussions for their input. The BPF would also like to thank all who submitted input through the BPF’s Call for contributions. Disclaimer: The IGF Secretariat has the honour to transmit this paper prepared by the 2019 Best Practice Forum on IoT, Big Data, AI. The content of the paper and the views expressed therein reflect the BPF discussions and are based on the various contributions received and do not imply any expression of opinion on the part of the United Nations. IGF2019 BPF IoT, Big Data, AI - Draft output report - November 2019 2 / 37 DOCUMENT REVIEW & FEEDBACK (editorial note 12 November 2019) The BPF IoT, Big Data, AI is inviting Community feedback on this draft report! How ? Please send your feedback to [email protected] Format? Feedback can be sent in an email or as a word or pdf document attached to an email. -
Artificial Intelligence and Big Data – Innovation Landscape Brief
ARTIFICIAL INTELLIGENCE AND BIG DATA INNOVATION LANDSCAPE BRIEF © IRENA 2019 Unless otherwise stated, material in this publication may be freely used, shared, copied, reproduced, printed and/or stored, provided that appropriate acknowledgement is given of IRENA as the source and copyright holder. Material in this publication that is attributed to third parties may be subject to separate terms of use and restrictions, and appropriate permissions from these third parties may need to be secured before any use of such material. ISBN 978-92-9260-143-0 Citation: IRENA (2019), Innovation landscape brief: Artificial intelligence and big data, International Renewable Energy Agency, Abu Dhabi. ACKNOWLEDGEMENTS This report was prepared by the Innovation team at IRENA’s Innovation and Technology Centre (IITC) with text authored by Sean Ratka, Arina Anisie, Francisco Boshell and Elena Ocenic. This report benefited from the input and review of experts: Marc Peters (IBM), Neil Hughes (EPRI), Stephen Marland (National Grid), Stephen Woodhouse (Pöyry), Luiz Barroso (PSR) and Dongxia Zhang (SGCC), along with Emanuele Taibi, Nadeem Goussous, Javier Sesma and Paul Komor (IRENA). Report available online: www.irena.org/publications For questions or to provide feedback: [email protected] DISCLAIMER This publication and the material herein are provided “as is”. All reasonable precautions have been taken by IRENA to verify the reliability of the material in this publication. However, neither IRENA nor any of its officials, agents, data or other third- party content providers provides a warranty of any kind, either expressed or implied, and they accept no responsibility or liability for any consequence of use of the publication or material herein. -
The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance Vs
The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance vs. Oracle Exadata By Krish Krishnan Sixth Sense Advisors Inc. November 2013 Sponsored by: Table of Contents Background ..................................................................3 Loading Data ............................................................3 Data Availability .........................................................3 Data Volumes ............................................................3 Storage Performance .....................................................4 Operational Costs ........................................................4 The Data Warehouse Appliance Selection .....................................5 Under the Hood ..........................................................5 Oracle Exadata. .5 Oracle Exadata Intelligent Storage ........................................6 Hybrid Columnar Compression ...........................................7 Mixed Workload Capability ...............................................8 Management ............................................................9 Scalability ................................................................9 Consolidation ............................................................9 Supportability ...........................................................9 Teradata Data Warehouse Appliance .........................................10 Hardware Improvements ................................................10 Teradata’s Intelligent Memory (TIM) vs. Exdata Flach Cache. -
Big Data Platforms, Tools, and Research at IBM
IBM Research Big Data Platforms, Tools, and Research at IBM Ed Pednault CTO, Scalable Analytics Business Analytics and Mathematical Sciences, IBM Research © 2011 IBM Corporation Please Note: IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. IBM Big Data Strategy: Move the Analytics Closer to the Data New analytic applications drive the Analytic Applications BI / Exploration / Functional Industry Predictive Content BI / requirements