IBM Data Warehousing. with IBM Business .Pdf

Total Page:16

File Type:pdf, Size:1020Kb

IBM Data Warehousing. with IBM Business .Pdf IBM® Data Warehousing with IBM Business Intelligence Tools Michael L. Gonzales The WILEY Dear Valued Customer, advantage We realize you’re a busy professional with deadlines to hit. Whether your goal is to learn a new technology or solve a critical problem, we want to be there to lend you a hand. Our primary objective is to provide you with the insight and knowledge you need to stay atop the highly competitive and ever- changing technology industry. Wiley Publishing, Inc., offers books on a wide variety of technical categories, including security, data warehousing, software development tools, and networking — everything you need to reach your peak. Regardless of your level of expertise, the Wiley family of books has you covered. • For Dummies – The fun and easy way to learn • The Weekend Crash Course –The fastest way to learn a new tool or technology • Visual – For those who prefer to learn a new topic visually • The Bible – The 100% comprehensive tutorial and reference • The Wiley Professional list – Practical and reliable resources for IT professionals The book you now hold, IBM Data Warehousing: With IBM Business Intelligence Tools, is the first comprehensive guide to the complete suite of IBM tools for data warehousing. Written by a leading expert, with contributions from key members of the IBM development teams that built these tools, the book is filled with detailed examples, as well as tips, tricks and workarounds for ensuring maximum performance. You can be assured that this is the most complete and authoritative guide to IBM data warehousing. Our commitment to you does not end at the last page of this book. We’d want to open a dialog with you to see what other solutions we can provide. Please be sure to visit us at www.wiley.com/compbooks to review our complete title list and explore the other resources we offer. If you have a comment, suggestion, or any other inquiry, please locate the “contact us” link at www.wiley.com. Finally, we encourage you to review the following page for a list of Wiley titles on related topics. Thank you for your support and we look forward to hearing from you and serving your needs again in the future. Sincerely, Richard K. Swadley Vice President & Executive Group Publisher Wiley Technology Publishing more information on related titles The Next Step in Data Warehousing Available from Wiley Publishing 0471384291 Create more 0471202436 powerful, flexible The official guide, data sharing written by the applications using a authors of the new XML-based Common Warehouse standard Metamodel INTERMEDIATE/ADVANCED 0471200522 An introduction to 0471219711 the standard for data The comprehensive warehouse guide to implement- integration ing SAP BW BEGINNER Available at your favorite bookseller or visit www.wiley.com/compbooks Advance Praise for IBM Data Warehousing “This book delivers both depth and breadth, a highly unusual combination in the business intelligence field. It not only describes the intricacies of var- ious IBM products, such as IBM DB2, IBM Intelligent Miner, and IBM DB2 OLAP, but it also sets the context for these products by providing a com- prehensive overview of data warehousing architecture, analytics, and data management.” Wayne Eckerson Director of Research, The Data Warehousing Institute “Organizations today are faced with a ‘data deluge’ about customers, sup- pliers, partners, employees and competitors. To survive and to prosper requires an increasing commitment to information management solutions. Michael Gonzales’ book provides an outstanding look at business intelli- gence software from IBM that can help companies excel through quicker, better-informed business decisions. In addition to a comprehensive explo- ration of IBM’s data warehouse, OLAP, data mining and spatial analysis capabilities, Michael clearly explains the organizational and data architec- ture underpinnings necessary for success in this information-intensive age.” Jeff Jones Senior Program Manager, IBM Data Management Solutions “IBM leads the way in delivering integrated, easy-to-use data warehous- ing, analysis and data management technology. This book delivers what every data warehousing professional needs most: a thorough overview of business intelligence fundamentals followed by solid practical advice on using IBM’s rich product suite to build, maintain and mine data warehouses.” Thomas W. Rosamilia Vice President, IBM Data Management (DB2) Worldwide Development IBM® Data Warehousing with IBM Business Intelligence Tools Michael L. Gonzales Publisher: Joe Wikert Executive Editor: Robert M. Elliott Assistant Developmental Editor: Emilie Herman Managing Editor: Micheline Frederick Media Development Specialist: Travis Silvers Text Design & Composition: Wiley Composition Services Designations used by companies to distinguish their products are often claimed as trade- marks. In all instances where Wiley Publishing, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appro- priate companies for more complete information regarding trademarks and registration. This book is printed on acid-free paper. ∞ Copyright © 2003 by Michael L. Gonzales. Copyright © 2003 IBM. Some text and illustrations. Published by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rose- wood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470. Requests to the Pub- lisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspointe Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4447, E-mail: [email protected]. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, inci- dental, consequential, or other damages. For general information on our other products and services please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Library of Congress Cataloging-in-Publication Data: ISBN: 0-471-13305-1 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To AMG2. Contents Acknowledgments xx Introduction xxiii Part One Fundamentals of Business Intelligence and the Data Warehouse 1 Chapter 1 Overview of the BI Organization 3 Overview of the BI Organization Architecture 4 Providing Information Content 10 Planning for Information Content 10 Designing for Information Content 13 Implementing Information Content 15 Justifying Your BI Effort 18 Linking Your Project to Known Business Requirements 18 Measuring ROI 18 Applying ROI 19 Questions for ROI Benefits 21 Making the Most of the First Iteration of the Warehouse 22 IBM and The BI Organization 22 Seamless Integration 23 Data Mining 24 Online Analytic Processing 24 Spatial Analysis 25 Database-Resident Tools 25 Simplified Data Delivery System 26 Zero-Latency 27 Summary 28 vii viii Contents Chapter 2 Business Intelligence Fundamentals 29 BI Components and Technologies 31 Business Intelligence Components 31 Data Warehouse 31 Data Sources 32 Data Targets 32 Warehouse Components 36 Extraction, Transformation, and Loading 37 Extraction 38 Transformation/Cleansing 39 Data Refining 39 Data Management 40 Data Access 40 Meta Data 41 Analytical User Requirements 42 Reporting and Querying 43 Online Analytical Processing 43 Multidimensional Views 44 Calculation-Intensive Capabilities 45 Time Intelligence 45 Statistics 46 Data Mining 46 Dimensional Technology and BI 47 The OLAP Server 48 MOLAP 49 ROLAP 50 Defining the Dimensional Spectrum 50 Touch Points 52 Zero-Latency and Your Warehouse Environment 53 Closed-Loop Learning 53 Historical Integrity 54 Summary 58 Chapter 3 Planning Data Warehouse Iterations 59 Planning Any Iteration 61 Building Your BI Plan 62 Enterprise Strategy 63 Designing the Technical Architecture 64 Designing the Data Architecture 66 Implementing and Maintaining the Warehouse 69 Planning the First Iteration 70 Aligning the Warehouse with Corporate Strategy 71 Conducting a Readiness Assessment 71 Resource Planning 74 Identifying Opportunities with the DIF Matrix1 77 Determining the Right Approach 78 Applying the DIF Matrix 78 Antecedent Documentation and Known Problems 80 Contents ix IT JAD Sessions 80 Select Candidate Iteration Opportunities 80 Get IT Scores 81
Recommended publications
  • Table of Contents
    The Kimball Group Reader Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection Ralph Kimball and Margy Ross with Bob Becker, Joy Mundy, and Warren Thornthwaite Contents Introduction . xxv 1 The Reader at a Glance . 1 Setting Up for Success . 1 1.1 Resist the Urge to Start Coding . 1 1.2 Set Your Boundaries . 4 Tackling DW/BI Design and Development . 6 1.3 Data Wrangling . 6 1.4 Myth Busters . 9 1.5 Dividing the World . 10 1.6 Essential Steps for the Integrated Enterprise Data Warehouse . 13 1.7 Drill Down to Ask Why . 22 1.8 Slowly Changing Dimensions . 25 1.9 Judge Your BI Tool through Your Dimensions . 28 1.10 Fact Tables . 31 1.11 Exploit Your Fact Tables . 33 2 Before You Dive In . 35 Before Data Warehousing . 35 2.1 History Lesson on Ralph Kimball and Xerox PARC. 36 Historical Perspective . 37 2.2 The Database Market Splits . 37 2.3 Bringing Up Supermarts . 40 Dealing with Demanding Realities . 47 2.4 Brave New Requirements for Data Warehousing . 47 2.5 Coping with the Brave New Requirements. 52 2.6 Stirring Things Up . 57 2.7 Design Constraints and Unavoidable Realities . 60 xiv Contents 2.8 Two Powerful Ideas . 64 2.9 Data Warehouse Dining Experience . 67 2.10 Easier Approaches for Harder Problems . 70 2.11 Expanding Boundaries of the Data Warehouse . 72 3 Project/Program Planning . 75 Professional Responsibilities . 75 3.1 Professional Boundaries . 75 3.2 An Engineer’s View . 78 3.3 Beware the Objection Removers .
    [Show full text]
  • Basically Speaking, Inmon Professes the Snowflake Schema While Kimball Relies on the Star Schema
    What is the main difference between Inmon and Kimball? Basically speaking, Inmon professes the Snowflake Schema while Kimball relies on the Star Schema. According to Ralf Kimball… Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtained from the dimension modeling on a local departmental level. He follows Bottom-up approach i.e. first creates individual Data Marts from the existing sources and then Create Data Warehouse. KIMBALL – First Data Marts – Combined way – Data warehouse. According to Bill Inmon… Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary. He follows Top-down approach i.e. first creates Data Warehouse from the existing sources and then create individual Data Marts. INMON – First Data warehouse – Later – Data Marts. The Main difference is: Kimball: follows Dimensional Modeling. Inmon: follows ER Modeling bye Mayee. Kimball: creating data marts first then combining them up to form a data warehouse. Inmon: creating data warehouse then data marts. What is difference between Views and Materialized Views? Views: •• Stores the SQL statement in the database and let you use it as a table. Every time you access the view, the SQL statement executes. •• This is PSEUDO table that is not stored in the database and it is just a query.
    [Show full text]
  • MASTER's THESIS Role of Metadata in the Datawarehousing Environment
    2006:24 MASTER'S THESIS Role of Metadata in the Datawarehousing Environment Kranthi Kumar Parankusham Ravinder Reddy Madupu Luleå University of Technology Master Thesis, Continuation Courses Computer and Systems Science Department of Business Administration and Social Sciences Division of Information Systems Sciences 2006:24 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--06/24--SE Preface This study is performed as the part of the master’s programme in computer and system sciences during 2005-06. It has been very useful and valuable experience and we have learned a lot during the study, not only about the topic at hand but also to manage to the work in the specified time. However, this workload would not have been manageable if we had not received help and support from a number of people who we would like to mention. First of all, we would like to thank our professor Svante Edzen for his help and supervision during the writing of thesis. And also we would like to express our gratitude to all the employees who allocated their valuable time to share their professional experience. On a personal level, Kranthi would like to thank all his family for their help and especially for my friends Kiran, Chenna Reddy, and Deepak Kumar. Ravi would like to give the greatest of thanks to his family for always being there when needed, and constantly taking so extremely good care of me….Also, thanks to all my friends for being close to me. Luleå University of Technology, 31 January 2006 Kranthi Kumar Parankusham Ravinder Reddy Madupu Abstract In order for a well functioning data warehouse to succeed many components must work together.
    [Show full text]
  • Kimball Vs. Inmon
    © 2011 - Andy Hogg Kimball vs. Inmon "Neither are any wars so furious and bloody, or of so long continuance as those occasioned by difference in opinion, especially if it be in things indifferent." (Swift, 1726) In the world of the data warehouse (DW) there are two dominant and opposing dogmas. Zealots of both extoll the virtues of their chosen doctrine with religious fervour, whilst decrying the beliefs of the other. These doctrines have existed for years, and in that time innumerable DWs have been built upon the principles of William Inmon. Likewise, an incalculable number built upon the ideas of Ralph Kimball. Inmon’s Corporate Information Factory (CIF), is a top-down approach. Since the whole DW is built in advance of usage, it requires significant time to deliver value. It therefore requires unwavering sponsorship from a senior figure within the organisation, possessing long-term vision of the DW’s value. Commentators contrast Kimball’s Bus Architecture (BA) as a bottom-up approach, where the data marts (DMs) are built first and unified into a DW at the end of the process. Inmon (n.d.a) ridicules this:- “…in bottom up data warehouse development first one data mart is developed, then another data mart is developed, then one day - presto - you magically and effortlessly wake up and have a data warehouse”. Kimball (2003) dislikes the bottom-up description, “Bottom-up is typically viewed as quick and dirty – focused on the needs of a single department rather than the enterprise”. He maintains the BA is a holistic view of the enterprise, with a final overall structure planned from the outset.
    [Show full text]
  • Educational Open Government Data: from Requirements to End Users
    Educational Open Government Data: from requirements to end users Rudolf Eckelberg, Vytor Bezerra Calixto, Marina Hoshiba Pimentel, Marcos Didonet Del Fabro, Marcos Suny´e,Leticia M. Peres, Eduardo Todt, Thiago Alves, Adriana Dragone, and Gabriela Schneider C3SL and NuPE Labs Federal University of Paran´a,Curitiba, Brazil {rce16,vsbc14,marina,marcos.ddf,sunye,lmperes,todt}@inf.ufpr.br, [email protected],[email protected],[email protected] Abstract. The large availability of open government data raises enor- mous opportunities for open big data analytics. However, providing an end-to-end framework able to handle tasks from data extraction and pro- cessing to a web interface involves many challenges. One critical factor is the existence of many players with different knowledge, who need to interact, such as application domain experts, database designers, and web developers. This represents a knowledge gap that is difficult to over- come. In this paper, we present a case study for big data analytics over Brazilian educational data, with more than 1 billion records. We show how we organized the data analytics phase, starting from the analytics requirements, data evolution, development and deployment in a public interface. Keywords: Open Government Data; Analytics API; data evolution 1 Introduction The large availability of Open Governmental Data raises enormous opportunities for open big data analytics. Opportunities are always followed by challenges and when one handle Big Data, difficulties lie in data capture, storage, searching, sharing, analysis, and visualization [2]. Providing useful Open Data would need a complete team, from the domain expert up to the web developer, so often it cannot be handled only by a data scientist for example.
    [Show full text]
  • Lecture @Dhbw: Data Warehouse Part I: Introduction to Dwh and Dwh Architecture Andreas Buckenhofer, Daimler Tss About Me
    A company of Daimler AG LECTURE @DHBW: DATA WAREHOUSE PART I: INTRODUCTION TO DWH AND DWH ARCHITECTURE ANDREAS BUCKENHOFER, DAIMLER TSS ABOUT ME Andreas Buckenhofer https://de.linkedin.com/in/buckenhofer Senior DB Professional [email protected] https://twitter.com/ABuckenhofer https://www.doag.org/de/themen/datenbank/in-memory/ Since 2009 at Daimler TSS Department: Big Data http://wwwlehre.dhbw-stuttgart.de/~buckenhofer/ Business Unit: Analytics https://www.xing.com/profile/Andreas_Buckenhofer2 NOT JUST AVERAGE: OUTSTANDING. As a 100% Daimler subsidiary, we give 100 percent, always and never less. We love IT and pull out all the stops to aid Daimler's development with our expertise on its journey into the future. Our objective: We make Daimler the most innovative and digital mobility company. Daimler TSS INTERNAL IT PARTNER FOR DAIMLER + Holistic solutions according to the Daimler guidelines + IT strategy + Security + Architecture + Developing and securing know-how + TSS is a partner who can be trusted with sensitive data As subsidiary: maximum added value for Daimler + Market closeness + Independence + Flexibility (short decision making process, ability to react quickly) Daimler TSS 4 LOCATIONS Daimler TSS Germany 7 locations 1000 employees* Ulm (Headquarters) Daimler TSS China Stuttgart Hub Beijing 10 employees Berlin Karlsruhe Daimler TSS Malaysia Hub Kuala Lumpur 42 employees Daimler TSS India * as of August 2017 Hub Bangalore 22 employees Daimler TSS Data Warehouse / DHBW 5 DWH, BIG DATA, DATA MINING This lecture is
    [Show full text]
  • Overview of the Corporate Information Factory and Dimensional Modeling
    Overview of the Corporate Information Factory and Dimensional Modeling Copyright (c) 2010 BIS3 LLC. All rights reserved. Data Warehouse Architect: Rosendo Abellera ` President, BIS3 o Nearly 2 decades software and system development o 12 years in DW and BI space o 25+ years of data and intelligence/analytics Other Notable Data Projects: ` Accenture ` Toshiba ` National Security Agency (NSA) ` US Air Force Copyright (c) 2010 BIS3 LLC. All rights reserved. 2 A data warehouse is a repository of an organization's electronically stored data designed to facilitate reporting and analysis. Subject-oriented Non-volatile Integrated Time-variant Reference: Wikipedia Copyright (c) 2010 BIS3 LLC. All rights reserved. 3 Copyright (c) 2010 BIS3 LLC. All rights reserved. 4 3rd Normal Form Bill Inmon Data Mart Corporate ? Enterprise Data ? Warehouse Dimensional Information Hub and Spoke Operational Data Modeling Factory Store Ralph Kimball Slowly Changing Dimensions Snowflake Star Schema Copyright (c) 2010 BIS3 LLC. All rights reserved. 5 ` Corporate ` Dimensional Information Factory Modeling 1.Top down 1.Bottom up 2.Data normalized to 2.Data denormalized to 3rd Normal Form form star schema 3.Enterprise data 3.Data marts conform to warehouse spawns develop the enterprise data marts data warehouse Copyright (c) 2010 BIS3 LLC. All rights reserved. 6 ` Focus ◦ Single repository of enterprise data ◦ Framework for Decision Support Systems (DSS) ` Specifics ◦ Create specific structures for distinct purpose ◦ Model data in 3rd Normal Form ◦ As a Hub and Spoke Approach, create data marts as subsets of data warehouse as needed Copyright (c) 2010 BIS3 LLC. All rights reserved. 7 Copyright (c) 2010 BIS3 LLC.
    [Show full text]
  • The Data Warehouse ETL Toolkit
    P1: FCH/SPH P2: FCH/SPH QC: FCH/SPH T1: FCH WY046-FM WY046-Kimball-v4.cls August 18, 2004 13:42 The Data Warehouse ETL Toolkit i P1: FCH/SPH P2: FCH/SPH QC: FCH/SPH T1: FCH WY046-FM WY046-Kimball-v4.cls August 18, 2004 13:42 ii P1: FCH/SPH P2: FCH/SPH QC: FCH/SPH T1: FCH WY046-FM WY046-Kimball-v4.cls August 18, 2004 13:42 The Data Warehouse ETL Toolkit Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data Ralph Kimball Joe Caserta Wiley Publishing, Inc. iii P1: FCH/SPH P2: FCH/SPH QC: FCH/SPH T1: FCH WY046-FM WY046-Kimball-v4.cls August 18, 2004 13:42 Published by Wiley Publishing, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www.wiley.com Copyright C 2004 by Wiley Publishing, Inc. All rights reserved. Published simultaneously in Canada eISBN: 0-764-57923-1 Printed in the United States of America 10987654321 No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, ex- cept as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspoint Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4355, e-mail: [email protected].
    [Show full text]
  • Raquel Gouveia Ribeiro Sistemas De Bases De Dados Orientados Por Colunas
    Escola de Engenharia Raquel Gouveia Ribeiro Sistemas de Bases de Dados Orientados por Colunas Outubro de 2013 Escola de Engenharia Departamento de Informática Raquel Gouveia Ribeiro Sistemas de Bases de Dados Orientados por Colunas Dissertação de Mestrado Mestrado em Engenharia Informática Trabalho realizado sob orientação de Professor Doutor Orlando Manuel de Oliveira Belo Outubro de 2013 À minha família. iii Agradecimentos Agradeço em primeiro lugar ao meu orientador Professor Orlando Belo, por toda a colaboração e dedicação que me disponibilizou na realização desta dissertação. Foi a pessoa que sempre me ensinou a dar o passo seguinte e que me fez crescer. Muito obrigada pela paciência demonstrada em diversos assuntos e pelos seus conselhos, aos quais muitas vezes recorri, pela sua vasta experiência e respeito pelo seu trabalho e pessoa. Queria também agradecer à minha família, por toda a dedicação, apoio e por todos os momentos em que me fizeram ver a realidade, fosse ela dura ou não. Por todas as alturas que me proporcionaram as condições de trabalho necessárias, por todo o encorajamento e palavras que me fizeram acreditar. É graças sobretudo a eles, que encarei esta fase da minha vida sempre com confiança que era capaz, e com o lema de que se deve olhar em frente e nunca para trás. Muito obrigado! iv v Resumo Sistemas de Bases de Dados Orientados por Colunas Nos últimos tempos os sistemas de bases de dados orientados por colunas têm atraído muita atenção, quer por parte de investigadores e modeladores de base de dados, quer por profissionais da área, com particular interesse em aspectos relacionados com arquiteturas, desempenho dos sistemas e sua escalabilidade ou em aplicações de suporte à decisão, nomeadamente em Data Warehousing e Business Inteligence.
    [Show full text]
  • Dimensional Modeling: Kimball Fundamentals & Advanced
    Dimensional Modeling: Kimball Fundamentals & Advanced Techniques Why Attend Excellence in dimensional modeling remains the keystone of a well-designed data warehouse/business intelligence system, regardless of whether you’ve adopted a Kimball, Corporate Information Factory, or hybrid architecture. The Data Warehouse Toolkit established an extensive portfolio of dimensional techniques and vocabulary, including conformed dimensions, slowly changing dimensions, junk dimensions, mini-dimensions, bridge tables, periodic and accumulating snapshot fact tables, and the list goes on. In this course, you will learn practical dimensional modeling techniques covering fundamental to advanced patterns and best practices. Concepts are illustrated through real-world industry scenarios, conveyed via a combination of lectures, through a combination of lectures, class exercises, small group workshops, and individual problem solving. Bringing DecisionWorks onsite enables everyone on the team to get on the same page with a common vocabulary and understanding of core techniques. The result is more effective and efficient education with lower travel cost and lost productivity, plus less downstream “tire spinning” within the team. Who Should Attend This course is primarily intended for DW/BI team members who’ve had prior exposure to dimensional modeling. The first day is appropriate for anyone on the team, including project managers, data warehouse architects, data modelers, database administrators, business analysts, and ETL or BI application developers and
    [Show full text]
  • Rethinking EDW in the Era of Expansive Information Management
    3/14/2011 Rethinking EDW In The Era Of Expansive Information Management Ralph Kimball Informatica March 2011 Ralph Kimball Informatica March 2011 Trends Driving the Expansion Of The EDW in 2011 The big business trends big data big expectations Management appreciates role of information assets: drive revenue growth, conversion rate, cost reduction, asset efficiency (doing more with less) Competitors are monetizing opportunities, management is noticing! Social media analysis, especially sentiment analysis becoming mainstream The EDW has become operational: Real time data drives intervention, feature development, strategic planning Irresistible push to zero latency data delivery Delivery modes have exploded BI everywhere Smart phones Tablets PCs at work, on the airplane, at home Delivery to operational/control user interfaces, portals, alerts, tweets 1 3/14/2011 The EDW Must Change Importance and critical scrutiny of the EDW is growing as organizations monetize the decisions made with their data The size and types of the data being fed to the EDW is exploding – much new “big data” is not suited for relational processing EDW must adapt to different architectural roles: source or target, centralized or distributed, batch or real-time Many new unfamiliar technologies are required to exploit the data and stay competitive – need to shift the mindset to design for the unknown IT management is faced with an unprecedented array of choices and not much time to evaluate Increased Scope of the EDW No longer a library for simple
    [Show full text]
  • The Technology of Using a Data Warehouse to Support Decision -Making in Health Care
    International Journal of Database Management Systems ( IJDMS ) Vol.5, No.3, June 2013 THE TECHNOLOGY OF USING A DATA WAREHOUSE TO SUPPORT DECISION -MAKING IN HEALTH CARE Dr. Osama E.Sheta 1 and Ahmed Nour Eldeen 2 1,2 Department of Mathematics (Computer Science) Faculty of Science, Zagazig University, Zagazig, Elsharkia, Egypt. 1 2 oesheta75@gmail .com, [email protected] ABSTRACT: This paper describes the technology of data warehouse in healthcare decision-making and tools for support of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors needs information about and insight into the existing health data, so as to make decision more efficiently without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a complex problem during the healthcare decision-making process. To solve this problem, the building a healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers and doctors can view data from more than one perspective with reduced query time, thus making decisions faster and more comprehensive. KEYWORDS : Healthcare data warehouse, Extract-Transformation-Load (ETL), Cancer data warehouse, On-Line Analysis Processing (OLAP), Multidimensional Expression (MDX) and Health Insurance Organization(HIO). 1. I NTRODUCTION Managing data in healthcare organizations has become a challenge as a result of healthcare managers having considerable differences in objectives, concerns, priorities and constraints.
    [Show full text]