Data Warehousing Fundamentals: A Comprehensive Guide for IT ProfessionalsEine Einführung in das Data Warehousing - speziell für IT-Fachleute, die sich in dieses Gebiet einarbeiten wollen. Behandelt werden alle wichtigen Themen wie Planung, Systemvoraussetzungen, Architektur, Infrastruktur, Design, Datenaufbereitung, Implementation und Wartung. Der Stoff wird anhand zahlreicher Beispiele, Fallstudien aus der Industrie und Übungsaufgaben anschaulich und nachvollziehbar dargestellt. Autor Paulraj Ponniah verfügt über 25 Jahre Erfahrung in Design und Implementation von Datenbanken und Data Warehousing Anwendungen. Er hat u.a. so namhafte Unternehmen wie Texaco, Sotheby's, Blue Cross/Blue Shield, NA Philips und Bantam-Doubleday-Dell betreut. "Data Warehousing Fundamentals" - ein topaktuelles Buch zu einem brisanten Thema. |
From inside the book
Results 1-5 of 79
Page x
... Techniques 99 1 Adapting the JAD Methodology 102 1 Review of Existing Documentation 103 1 Requirements Definition: Scope and Content 104 1 Data Sources 105 1 Data Transformation 105 1 Data Storage 105 1 Information Delivery 105 1 ...
... Techniques 99 1 Adapting the JAD Methodology 102 1 Review of Existing Documentation 103 1 Requirements Definition: Scope and Content 104 1 Data Sources 105 1 Data Transformation 105 1 Data Storage 105 1 Information Delivery 105 1 ...
Page xiv
... Techniques 263 1 2 Evaluation of the Techniques 270 13 Data Transformation 271 1 2 Data Transformation: Basic Tasks xiv CONTENTS.
... Techniques 263 1 2 Evaluation of the Techniques 270 13 Data Transformation 271 1 2 Data Transformation: Basic Tasks xiv CONTENTS.
Page xv
... Techniques and Processes 1 2 Data Refresh Versus Update 282 1 2 Procedure for Dimension Tables 280 283 1 2 Fact Tables: History and Incremental Loads ETL Summary 285 1 2 ETL Tool Options 285 1 2 Reemphasizing ETL Metadata 286 1 2 ETL ...
... Techniques and Processes 1 2 Data Refresh Versus Update 282 1 2 Procedure for Dimension Tables 280 283 1 2 Fact Tables: History and Incremental Loads ETL Summary 285 1 2 ETL Tool Options 285 1 2 Reemphasizing ETL Metadata 286 1 2 ETL ...
Page xviii
... Techniques 408 1 2 Cluster Detection 409 1 2 Decision Trees 411 1 2 Memory-Based Reasoning 413 1 2 LinkAnalysis 415 1 2 Neural Networks 417 1 2 Genetic Algorithms 418 1 2 Moving into Data Mining 419 Data Mining Applications 422 1 2 ...
... Techniques 408 1 2 Cluster Detection 409 1 2 Decision Trees 411 1 2 Memory-Based Reasoning 413 1 2 LinkAnalysis 415 1 2 Neural Networks 417 1 2 Genetic Algorithms 418 1 2 Moving into Data Mining 419 Data Mining Applications 422 1 2 ...
Page xix
... Techniques 1 2 Data Partitioning 449 1 2 Data Clustering 450 1 2 Parallel Processing 450 449 1 2 Summary Levels 451 1 2 Referential Integrity Checks 451 1 2 Initialization Parameters 451 1 2 Data Arrays 452 Chapter Summary 452 Review ...
... Techniques 1 2 Data Partitioning 449 1 2 Data Clustering 450 1 2 Parallel Processing 450 449 1 2 Summary Levels 451 1 2 Referential Integrity Checks 451 1 2 Initialization Parameters 451 1 2 Data Arrays 452 Chapter Summary 452 Review ...
Contents
1 | |
Part 2 PLANNING AND REQUIREMENTS | 63 |
Part 3 ARCHITECTURE AND INFRASTRUCTURE | 127 |
Part 4 DATA DESIGN AND DATA PREPARATION | 203 |
Part 5 INFORMATION ACCESS AND DELIVERY | 315 |
Part 6 IMPLEMENTATION AND MAINTENANCE | 429 |
Appendix A Project Life Cycle Steps and Checklists | 493 |
Appendix B Critical Factors for Success | 497 |
Appendix C Guidelines for Evaluating Vendor Solutions | 499 |
References | 501 |
Glossary | 503 |
Index | 511 |
Other editions - View all
Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals Paulraj Ponniah No preview available - 2004 |
Common terms and phrases
aggregate algorithms analysis applications architectural components attributes business dimensions capture changes chapter columns complex create data cleansing data elements data extraction data loading data marts data mining data model data quality data sources data staging data storage data structures data transformation data warehouse environment data warehouse project data warehousing database DBMS deployment dimension table dimensional model end-users enterprise example fact table Figure files functions hardware incremental loads integrated interface marketing MDDBs methods metrics MOLAP multidimensional OLAP system OLTP online analytical processing operational systems options package diagrams performance pilot platform predefined primary key product dimension programs project team queries and reports records relational requirements definition ROLAP selection server source data source systems specific staging area standards STAR schema summary techniques tion transaction types users values vendors ware Web-enabled data warehouse
Popular passages
Page 349 - Processing (OLAP) is a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
Page 18 - Inmon identified four characteristics of a data warehouse, which are represented in his formal definition: "... a data warehouse is a subject oriented, integrated, non-volatile and time variant collection of data in support of management's decisions.
Page 412 - Trees are normally drawn upside down, with the root at the top and the leaves at the bottom.
Page 501 - Kimball, Ralph, and Richard Merz. The Data Webhouse Toolkit: Building the WebEnabled Data Warehouse. New York: John Wiley & Sons, 2000.
Page 500 - Discovering Data Mining: From Concept to Implementation, Upper Saddle River, NJ: Prentice-Hall PTR, 1998.
Page 53 - It completes the process by providing users with knowledge to use the right information, at the right time, and at the right place.
Page 501 - Managing the Data Warehouse: Practical Techniques for Monitoring Operations and Performances, Administering Data and Tools, Managing Change and Growth, New York: Wiley, 1997.
Page 465 - ... but be careful not to bite off more than you can chew.
Page 5 - Web-enabled analysis tools enables merchants to gain insights into their customer base, manage inventories more tightly, and keep the right products in front of the right people at the right place at the right time.