Data Warehouse Design
|
|
|
- Brent Boone
- 10 years ago
- Views:
Transcription
1 Data Warehouse Design Modern Principles and Methodologies Matteo Golfarelli Stefano Rizzi Translated by Claudio Pagliarani Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto
2 Contents Acknowledgments Foreword Preface xiii xv xvii 1 Introduction to Data Warehousing 1.1 Decision Support Systems 1.2 Data Warehousing 1.3 Data Warehouse Architectures Single-Layer Architecture Two-Layer Architecture Three-Layer Architecture An Additional Architecture Classification 1.4 Data Staging and ETL Extraction Cleansing Transformation Loading 1.5 Multidimensional Model Restriction Aggregation 1.6 Meta-data 1.7 Accessing Data Warehouses Reports OLAP Dashboards 1.8 ROLAP, MOLAP, and HOLAP 1.9 Additional Issues Quality Security Evolution Data Warehouse System Lifecycle 2.1 Risk Factors 2.2 Тор-Down vs. Bottom-Up Business Dimensional Lifecycle Rapid Warehousing Methodology 2.3 Data Mart Design Phases Analysis and Reconciliation of Data Sources Requirement Analysis vii
3 Data Warehouse Design: Modern Principles and Methodologies Conceptual Design Workload Refinement and Validation of Conceptual Schemata Logical Design Physical Design Data-Staging Design Methodological Framework Scenario 1: Data-Driven Approach Scenario 2: Requirement-Driven Approach Scenario 3: Mixed Approach Testing Data Marts 58 3 Analysis and Reconciliation of Data Sources Inspecting and Normalizing Schemata The Integration Problem Different Perspectives Equivalent Modeling Constructs Incompatible Specifications Common Concepts Interrelated Concepts Integration Phases Preintegration Schema Comparison Schema Alignment Merging and Restructuring Schemata Defining Mappings 77 4 User Requirement Analysis Interviews Glossary-based Requirement Analysis Facts Preliminary Workload Goal-oriented Requirement Analysis Introduction to Tropos Organizational Modeling Decision-making Modeling Additional Requirements 97 5 Conceptual Modeling The Dimensional Fact Model: Basic Concepts Advanced Modeling Descriptive Attributes Cross-Dimensional Attributes Ill Convergence Shared Hierarchies Multiple Arcs Optional Arcs 115
4 Contents jx Incomplete Hierarchies Recursive Hierarchies Additivity Events and Aggregation Aggregating Additive Measures Aggregating Non-additive Measures Aggregating with Convergence and Cross-dimensional Attributes Aggregating with Optional or Multiple Arcs Empty Fact Schema Aggregation Aggregating with Functional Dependencies among Dimensions Aggregating along Incomplete or Recursive Hierarchies Time Transactional vs. Snapshot Schemata Late Updates Dynamic Hierarchies Overlapping Fact Schemata Formalizing the Dimensional Fact Model Metamodel Intensional Properties Extensional Properties Conceptual Design Entity-Relationship Schema-based Design Defining Facts Building Attribute Trees Pruning and Grafting Attribute Trees One-to-One Relationships Defining Dimensions Time Dimensions Defining Measures Generating Fact Schemata Relational Schema-based Design Defining Facts Building Attribute Trees Other Phases XML Schema-based Design Modeling XML Associations Preliminary Phases Selecting Facts and Building Attribute Trees Mixed-approach Design Mapping Requirements Building Fact Schemata Refining Requirement-driven Approach Design 196
5 X Data Warehouse Design: Modern Principles and Methodologies 7 Workload and Data Volume Workload Dimensional Expressions and Queries on Fact Schemata Drill-Across Queries Composite Queries Nested GPSJ Queries Validating a Workload in a Conceptual Schema Workload and Users Data Volumes Logical Modeling MOLAP and HOLAP Systems The Problem of Sparsity ROLAP Systems Star Schema Snowflake Schema Views Relational Schemata with Aggregate Data Temporal Scenarios Dynamic Hierarchies: Type Dynamic Hierarchies: Type Dynamic Hierarchies: Type Dynamic Hierarchies: Full Data Logging Deleting Tuples Logical Design From Fact Schemata to Star Schemata Descriptive Attributes Cross-dimensional Attributes Shared Hierarchies Multiple Arcs Optional Arcs Incomplete Hierarchies Recursive Hierarchies Degenerate Dimensions Additivity Issues Using Snowflake Schemata View Materialization Using Views to Answer Queries Problem Formalization A Materialization Algorithm View Fragmentation Vertical View Fragmentation Horizontal View Fragmentation 272
6 Contents xi 10 Data-staging Design Populating Reconciled Databases Extracting Data Transforming Data Loading Data Cleansing Data Dictionary-based Techniques Approximate Merging Ad-hoc Techniques Populating Dimension Tables Identifying the Data to Load Replacing Keys Populating Fact Tables Populating Materialized Views Indexes for the Data Warehouse B + -Tree Indexes Bitmap Indexes Bitmap Indexes vs. B + -Trees Advanced Bitmap Indexes Projection Indexes Join and Star Indexes Multi-join Indexes Spatial Indexes Join Algorithms Nested Loop Sort-merge Hash Join Physical Design Optimizers Rule-based Optimizers Cost-based Optimizers Histograms Index Selection Indexing Dimension Tables Indexing Fact Tables Additional Physical Design Elements Splitting a Database Into Tablespaces Allocating Data Files Disk Block Size Data Warehouse Project Documentation Data Warehouse Level Data Warehouse Schemata Deployment Schema 354
7 XU Data Warehouse Design: Modern Principles and Methodologies 13.2 Data Mart Level Bus and Overlapping Matrices Operational Schema Data-Staging Schema Domain Glossary Workload and Users Logical Schema and Physical Schema Testing Documents Fact Level Fact Schemata Attribute and Measure Glossaries Methodological Guidelines A Case Study Application Domain Planning the TranSport Data Warehouse The Sales Data Mart Data Source Analysis and Reconciliation User Requirement Analysis Conceptual Design Logical Design Data-Staging Design Physical Design The Marketing Data Mart Business Intelligence: Beyond the Data Warehouse Introduction to Business Intelligence Data Mining Association Rules Clustering Classifiers and Decision Trees Time Series What-If Analysis Inductive Techniques Deductive Techniques Methodological Notes Business Performance Management 417 Glossary 423 Bibliography 429 Index 445
Master Data Management and Data Governance Second Edition
Master Data Management and Data Governance Second Edition Alex Berson Larry Dubov Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore
How To Write A Diagram
Data Model ing Essentials Third Edition Graeme C. Simsion and Graham C. Witt MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF ELSEVIER AMSTERDAM BOSTON LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE
SAS BI Course Content; Introduction to DWH / BI Concepts
SAS BI Course Content; Introduction to DWH / BI Concepts SAS Web Report Studio 4.2 SAS EG 4.2 SAS Information Delivery Portal 4.2 SAS Data Integration Studio 4.2 SAS BI Dashboard 4.2 SAS Management Console
2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000
2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000 Introduction This course provides students with the knowledge and skills necessary to design, implement, and deploy OLAP
Data Warehouse: Introduction
Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of base and data mining group,
Overview. DW Source Integration, Tools, and Architecture. End User Applications (EUA) EUA Concepts. DW Front End Tools. Source Integration
DW Source Integration, Tools, and Architecture Overview DW Front End Tools Source Integration DW architecture Original slides were written by Torben Bach Pedersen Aalborg University 2007 - DWML course
Development Effort & Duration
Practical Software Project Estimation: A Toolkit for Estimating Software Development Effort & Duration International Software Benchmarking Standards Group Compiled and edited by Peter R. Hill Mc Grauu
Building and Managing
ORACLE Oracle Press' Building and Managing a Cloud Using Oracle Enterprise Manager 12c Madhup Gulati Adeesh Fulay Sudip Datta Mc Graw Hill Education New York Chicago San Francisco Lisbon London Madrid
Data Warehousing Systems: Foundations and Architectures
Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository
COURSE OUTLINE. Track 1 Advanced Data Modeling, Analysis and Design
COURSE OUTLINE Track 1 Advanced Data Modeling, Analysis and Design TDWI Advanced Data Modeling Techniques Module One Data Modeling Concepts Data Models in Context Zachman Framework Overview Levels of Data
Advanced Data Management Technologies
ADMT 2015/16 Unit 2 J. Gamper 1/44 Advanced Data Management Technologies Unit 2 Basic Concepts of BI and Data Warehousing J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Acknowledgements:
Data warehouse design
DataBase and Data Mining Group of DataBase and Data Mining Group of DataBase and Data Mining Group of Database and data mining group, Data warehouse design DATA WAREHOUSE: DESIGN - 1 Risk factors Database
Copyright 2007 Ramez Elmasri and Shamkant B. Navathe. Slide 29-1
Slide 29-1 Chapter 29 Overview of Data Warehousing and OLAP Chapter 29 Outline Purpose of Data Warehousing Introduction, Definitions, and Terminology Comparison with Traditional Databases Characteristics
Implementation & Administration
Microsoft SQL Server 2008 R2 Master Data Services: Implementation & Administration Tyler Graham Suzanne Selhorn Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi
Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier
Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.
M2074 - Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000 5 Day Course
Module 1: Introduction to Data Warehousing and OLAP Introducing Data Warehousing Defining OLAP Solutions Understanding Data Warehouse Design Understanding OLAP Models Applying OLAP Cubes At the end of
VISUALIZING DATA POWER VIEW. with MICROSOFT. Brian Larson. Mark Davis Dan English Paui Purington. Mc Grauu. Sydney Toronto
VISUALIZING DATA with MICROSOFT POWER VIEW Brian Larson Mark Davis Dan English Paui Purington Mc Grauu New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore
Management. Oracle Fusion Middleware. 11 g Architecture and. Oracle Press ORACLE. Stephen Lee Gangadhar Konduri. Mc Grauu Hill.
ORACLE Oracle Press Oracle Fusion Middleware 11 g Architecture and Management Reza Shafii Stephen Lee Gangadhar Konduri Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan
Compensating the Sales Force
Compensating the Sales Force A Practical Guide to Designing Winning Sales Reward Programs Second Edition David J. Cichelli Me Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan
Delivering Business Intelligence With Microsoft SQL Server 2005 or 2008 HDT922 Five Days
or 2008 Five Days Prerequisites Students should have experience with any relational database management system as well as experience with data warehouses and star schemas. It would be helpful if students
Part 22. Data Warehousing
Part 22 Data Warehousing The Decision Support System (DSS) Tools to assist decision-making Used at all levels in the organization Sometimes focused on a single area Sometimes focused on a single problem
Data warehouse life-cycle and design
SYNONYMS Data Warehouse design methodology Data warehouse life-cycle and design Matteo Golfarelli DEIS University of Bologna Via Sacchi, 3 Cesena Italy [email protected] DEFINITION The term data
14. Data Warehousing & Data Mining
14. Data Warehousing & Data Mining Data Warehousing Concepts Decision support is key for companies wanting to turn their organizational data into an information asset Data Warehouse "A subject-oriented,
MDM and Data Warehousing Complement Each Other
Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There
SQL SERVER TRAINING CURRICULUM
SQL SERVER TRAINING CURRICULUM Complete SQL Server 2000/2005 for Developers Management and Administration Overview Creating databases and transaction logs Managing the file system Server and database configuration
The Data Warehouse Challenge
The Data Warehouse Challenge Taming Data Chaos Michael H. Brackett Technische Hochschule Darmstadt Fachbereichsbibliothek Informatik TU Darmstadt FACHBEREICH INFORMATIK B I B L I O T H E K Irwentar-Nr.:...H.3...:T...G3.ty..2iL..
Security Metrics. A Beginner's Guide. Caroline Wong. Mc Graw Hill. Singapore Sydney Toronto. Lisbon London Madrid Mexico City Milan New Delhi San Juan
Security Metrics A Beginner's Guide Caroline Wong Mc Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Contents FOREWORD
Data Warehousing with Oracle
Data Warehousing with Oracle Comprehensive Concepts Overview, Insight, Recommendations, Best Practices and a whole lot more. By Tariq Farooq A BrainSurface Presentation What is a Data Warehouse? Designed
Contents RELATIONAL DATABASES
Preface xvii Chapter 1 Introduction 1.1 Database-System Applications 1 1.2 Purpose of Database Systems 3 1.3 View of Data 5 1.4 Database Languages 9 1.5 Relational Databases 11 1.6 Database Design 14 1.7
Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing
COURSE 20463C: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER
Page 1 of 8 ABOUT THIS COURSE This 5 day course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL Server
Implementing a Data Warehouse with Microsoft SQL Server
Page 1 of 7 Overview This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL 2014, implement ETL
Oracle Database 11g: Data Warehousing Fundamentals
Oracle Database 11g: Data Warehousing Fundamentals Volume I Student Guide D56261GC10 Edition 1.0 February 2009 D58420 Author Lauran K. Serhal Technical Contributors and Reviewers David Allan Hermann Baer
1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing
1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 2. What is a Data warehouse a. A database application
Master Data Management
Master Data Management David Loshin AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO Ик^И V^ SAN FRANCISCO SINGAPORE SYDNEY TOKYO W*m k^ MORGAN KAUFMANN PUBLISHERS IS AN IMPRINT OF ELSEVIER
Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Alejandro Vaisman Esteban Zimanyi. Data. Warehouse. Systems. Design and Implementation. ^ Springer
Alejandro Vaisman Esteban Zimanyi Data Warehouse Systems Design and Implementation ^ Springer Contents Part I Fundamental Concepts 1 Introduction 3 1.1 A Historical Overview of Data Warehousing 4 1.2 Spatial
Data Integration and ETL Process
Data Integration and ETL Process Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, second
Fluency With Information Technology CSE100/IMT100
Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999
BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE. Prepared by:
BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE Cerulium Corporation has provided quality education and consulting expertise for over six years. We offer customized solutions to
Implementing a Data Warehouse with Microsoft SQL Server
This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse 2014, implement ETL with SQL Server Integration Services, and
Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006
Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006 What is a Data Warehouse? A data warehouse is a subject-oriented, integrated, time-varying, non-volatile
Implement a Data Warehouse with Microsoft SQL Server 20463C; 5 days
Lincoln Land Community College Capital City Training Center 130 West Mason Springfield, IL 62702 217-782-7436 www.llcc.edu/cctc Implement a Data Warehouse with Microsoft SQL Server 20463C; 5 days Course
Managing Data in Motion
Managing Data in Motion Data Integration Best Practice Techniques and Technologies April Reeve ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY
The Design and the Implementation of an HEALTH CARE STATISTICS DATA WAREHOUSE Dr. Sreèko Natek, assistant professor, Nova Vizija, srecko@vizija.
The Design and the Implementation of an HEALTH CARE STATISTICS DATA WAREHOUSE Dr. Sreèko Natek, assistant professor, Nova Vizija, [email protected] ABSTRACT Health Care Statistics on a state level is a
DATA WAREHOUSING AND OLAP TECHNOLOGY
DATA WAREHOUSING AND OLAP TECHNOLOGY Manya Sethi MCA Final Year Amity University, Uttar Pradesh Under Guidance of Ms. Shruti Nagpal Abstract DATA WAREHOUSING and Online Analytical Processing (OLAP) are
SQL Server 2012 Business Intelligence Boot Camp
SQL Server 2012 Business Intelligence Boot Camp Length: 5 Days Technology: Microsoft SQL Server 2012 Delivery Method: Instructor-led (classroom) About this Course Data warehousing is a solution organizations
Week 3 lecture slides
Week 3 lecture slides Topics Data Warehouses Online Analytical Processing Introduction to Data Cubes Textbook reference: Chapter 3 Data Warehouses A data warehouse is a collection of data specifically
Lean Supply Chain and Logistics Management
Lean Supply Chain and Logistics Management Paul Myerson Me Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto CONTENTS CHAPTER
BUSINESS INTELLIGENCE
SECOND EDITION BUSINESS INTELLIGENCE A MANAGERIAL APPROACH INTERNATIONAL EDITION Efraim Turban University of Hawaii Ramesh Sharda Oklahoma State University Dursun Deleii Oklahoma State University David
70-467: Designing Business Intelligence Solutions with Microsoft SQL Server
70-467: Designing Business Intelligence Solutions with Microsoft SQL Server The following tables show where changes to exam 70-467 have been made to include updates that relate to SQL Server 2014 tasks.
Bussiness Intelligence and Data Warehouse. Tomas Bartos CIS 764, Kansas State University
Bussiness Intelligence and Data Warehouse Schedule Bussiness Intelligence (BI) BI tools Oracle vs. Microsoft Data warehouse History Tools Oracle vs. Others Discussion Business Intelligence (BI) Products
IST722 Data Warehousing
IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr. Recall: Inmon s CIF The CIF is a reference architecture Understanding the Diagram The CIF is a reference architecture CIF
MIS636 AWS Data Warehousing and Business Intelligence Course Syllabus
MIS636 AWS Data Warehousing and Business Intelligence Course Syllabus I. Contact Information Professor: Joseph Morabito, Ph.D. Office: Babbio 419 Office Hours: By Appt. Phone: 201-216-5304 Email: [email protected]
A Design and implementation of a data warehouse for research administration universities
A Design and implementation of a data warehouse for research administration universities André Flory 1, Pierre Soupirot 2, and Anne Tchounikine 3 1 CRI : Centre de Ressources Informatiques INSA de Lyon
HYPERION MASTER DATA MANAGEMENT SOLUTIONS FOR IT
HYPERION MASTER DATA MANAGEMENT SOLUTIONS FOR IT POINT-AND-SYNC MASTER DATA MANAGEMENT 04.2005 Hyperion s new master data management solution provides a centralized, transparent process for managing critical
OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP
Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key
Building Cubes and Analyzing Data using Oracle OLAP 11g
Building Cubes and Analyzing Data using Oracle OLAP 11g Collaborate '08 Session 219 Chris Claterbos [email protected] Vlamis Software Solutions, Inc. 816-729-1034 http://www.vlamis.com Copyright 2007,
Data Warehousing Fundamentals for IT Professionals. 2nd Edition
Brochure More information from http://www.researchandmarkets.com/reports/2171973/ Data Warehousing Fundamentals for IT Professionals. 2nd Edition Description: Cutting-edge content and guidance from a data
Software and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth
Smart Process Plants Software and Hardware Solutions for Accurate Data and Profitable Operations Miguel J. Bagajewicz, Ph.D. University of Oklahoma Donald J. Chmielewski Contributor DuyQuang Nguyen Tanth
LEARNING SOLUTIONS website milner.com/learning email [email protected] phone 800 875 5042
Course 20467A: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Length: 5 Days Published: December 21, 2012 Language(s): English Audience(s): IT Professionals Overview Level: 300
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012 OVERVIEW About this Course Data warehousing is a solution organizations use to centralize business data for reporting and analysis.
Chapter 5. Warehousing, Data Acquisition, Data. Visualization
Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives
SENG 520, Experience with a high-level programming language. (304) 579-7726, [email protected]
Course : Semester : Course Format And Credit hours : Prerequisites : Data Warehousing and Business Intelligence Summer (Odd Years) online 3 hr Credit SENG 520, Experience with a high-level programming
Building a Data Warehouse
Building a Data Warehouse With Examples in SQL Server EiD Vincent Rainardi BROCHSCHULE LIECHTENSTEIN Bibliothek Apress Contents About the Author. ; xiij Preface xv ^CHAPTER 1 Introduction to Data Warehousing
Dimensional Modeling for Data Warehouse
Modeling for Data Warehouse Umashanker Sharma, Anjana Gosain GGS, Indraprastha University, Delhi Abstract Many surveys indicate that a significant percentage of DWs fail to meet business objectives or
Implementing a Data Warehouse with Microsoft SQL Server 2012
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012 Length: Audience(s): 5 Days Level: 200 IT Professionals Technology: Microsoft SQL Server 2012 Type: Delivery Method: Course Instructor-led
BUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT
BUILDING BLOCKS OF DATAWAREHOUSE G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT 1 Data Warehouse Subject Oriented Organized around major subjects, such as customer, product, sales. Focusing on
Implementing a Data Warehouse with Microsoft SQL Server MOC 20463
Implementing a Data Warehouse with Microsoft SQL Server MOC 20463 Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing
COURSE OUTLINE MOC 20463: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER
COURSE OUTLINE MOC 20463: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER MODULE 1: INTRODUCTION TO DATA WAREHOUSING This module provides an introduction to the key components of a data warehousing
DATA WAREHOUSE E KNOWLEDGE DISCOVERY
DATA WAREHOUSE E KNOWLEDGE DISCOVERY Prof. Fabio A. Schreiber Dipartimento di Elettronica e Informazione Politecnico di Milano DATA WAREHOUSE (DW) A TECHNIQUE FOR CORRECTLY ASSEMBLING AND MANAGING DATA
Data warehouse Architectures and processes
Database and data mining group, Data warehouse Architectures and processes DATA WAREHOUSE: ARCHITECTURES AND PROCESSES - 1 Database and data mining group, Data warehouse architectures Separation between
Implementing a Data Warehouse with Microsoft SQL Server 2012 (70-463)
Implementing a Data Warehouse with Microsoft SQL Server 2012 (70-463) Course Description Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This five-day
Data warehousing with PostgreSQL
Data warehousing with PostgreSQL Gabriele Bartolini http://www.2ndquadrant.it/ European PostgreSQL Day 2009 6 November, ParisTech Telecom, Paris, France Audience
Implementing a Data Warehouse with Microsoft SQL Server 2012
Course 10777 : Implementing a Data Warehouse with Microsoft SQL Server 2012 Page 1 of 8 Implementing a Data Warehouse with Microsoft SQL Server 2012 Course 10777: 4 days; Instructor-Led Introduction Data
Implementing a Data Warehouse with Microsoft SQL Server 2012
Implementing a Data Warehouse with Microsoft SQL Server 2012 Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations Considerations for a Data Warehouse
Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina
Data Warehousing Read chapter 13 of Riguzzi et al Sistemi Informativi Slides derived from those by Hector Garcia-Molina What is a Warehouse? Collection of diverse data subject oriented aimed at executive,
Oracle JDeveloper 10g for Forms & PL/SQL
ORACLE Oracle Press Oracle JDeveloper 10g for Forms & PL/SQL Peter Koletzke Duncan Mills Me Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore
Data Warehousing and OLAP
1 Data Warehousing and OLAP Hector Garcia-Molina Stanford University Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots
Course 20463:Implementing a Data Warehouse with Microsoft SQL Server
Course 20463:Implementing a Data Warehouse with Microsoft SQL Server Type:Course Audience(s):IT Professionals Technology:Microsoft SQL Server Level:300 This Revision:C Delivery method: Instructor-led (classroom)
Implementing a Data Warehouse with Microsoft SQL Server
CÔNG TY CỔ PHẦN TRƯỜNG CNTT TÂN ĐỨC TAN DUC INFORMATION TECHNOLOGY SCHOOL JSC LEARN MORE WITH LESS! Course 20463 Implementing a Data Warehouse with Microsoft SQL Server Length: 5 Days Audience: IT Professionals
East Asia Network Sdn Bhd
Course: Analyzing, Designing, and Implementing a Data Warehouse with Microsoft SQL Server 2014 Elements of this syllabus may be change to cater to the participants background & knowledge. This course describes
Understanding Data Warehousing. [by Alex Kriegel]
Understanding Data Warehousing 2008 [by Alex Kriegel] Things to Discuss Who Needs a Data Warehouse? OLTP vs. Data Warehouse Business Intelligence Industrial Landscape Which Data Warehouse: Bill Inmon vs.
Implementing a Data Warehouse with Microsoft SQL Server
Course Code: M20463 Vendor: Microsoft Course Overview Duration: 5 RRP: 2,025 Implementing a Data Warehouse with Microsoft SQL Server Overview This course describes how to implement a data warehouse platform
Data Warehousing Fundamentals Student Guide
Data Warehousing Fundamentals Student Guide D16310GC10 Edition 1.0 September 2002 D37302 Authors Nikos Psomas Padmaja Mitravinda, Kolachalam Technical Contributors and Reviewers Kasturi Shekhar Vidya Nagaraj
Microsoft Data Warehouse in Depth
Microsoft Data Warehouse in Depth 1 P a g e Duration What s new Why attend Who should attend Course format and prerequisites 4 days The course materials have been refreshed to align with the second edition
Data Warehousing in the Age of Big Data
Data Warehousing in the Age of Big Data Krish Krishnan AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD * PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Morgan Kaufmann is an imprint of Elsevier
Mastering Data Warehouse Aggregates. Solutions for Star Schema Performance
Brochure More information from http://www.researchandmarkets.com/reports/2248199/ Mastering Data Warehouse Aggregates. Solutions for Star Schema Performance Description: - This is the first book to provide
Subject Description Form
Subject Description Form Subject Code Subject Title COMP417 Data Warehousing and Data Mining Techniques in Business and Commerce Credit Value 3 Level 4 Pre-requisite / Co-requisite/ Exclusion Objectives
Microsoft. Course 20463C: Implementing a Data Warehouse with Microsoft SQL Server
Course 20463C: Implementing a Data Warehouse with Microsoft SQL Server Length : 5 Days Audience(s) : IT Professionals Level : 300 Technology : Microsoft SQL Server 2014 Delivery Method : Instructor-led
Data Warehouse Overview. Srini Rengarajan
Data Warehouse Overview Srini Rengarajan Please mute Your cell! Agenda Data Warehouse Architecture Approaches to build a Data Warehouse Top Down Approach Bottom Up Approach Best Practices Case Example
A Service-oriented Architecture for Business Intelligence
A Service-oriented Architecture for Business Intelligence Liya Wu 1, Gilad Barash 1, Claudio Bartolini 2 1 HP Software 2 HP Laboratories {[email protected]} Abstract Business intelligence is a business
