《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 02 Data Warehousing

Business Intelligence: A Managerial Perspective on Analytics(3rd Edition) INTELLIGENCE A Managerial Perspective on Analytics Chapter 2: RAMESH SHAL EFRAUTI RRAN Data Warehousing

Chapter 2: Data Warehousing Business Intelligence: A Managerial Perspective on Analytics (3rd Edition)

Learning objectives Understand the basic definitions and concepts of data warehouses Learn different types of data warehousing architectures; their comparative advantages and disadvantages Describe the processes used in developing and managing data warehouses Explain data warehousing operations Continued.) Copynight@ 2014 Pearson Education, Inc Slide 2-2

Copyright © 2014 Pearson Education, Inc. Slide 2- 2 Learning Objectives ▪ Understand the basic definitions and concepts of data warehouses ▪ Learn different types of data warehousing architectures; their comparative advantages and disadvantages ▪ Describe the processes used in developing and managing data warehouses ▪ Explain data warehousing operations (Continued…)

Learning Objectives Explain the role of data warehouses in decision support Explain data integration and the extraction, transformation, and load(ETL processes Describe real-time(ak a. right-time and/or active)data warehousing Understand data warehouse administration and security issues Copynight@ 2014 Pearson Education, Inc Slide 2-3

Copyright © 2014 Pearson Education, Inc. Slide 2- 3 Learning Objectives ▪ Explain the role of data warehouses in decision support ▪ Explain data integration and the extraction, transformation, and load (ETL) processes ▪ Describe real-time (a.k.a. right-time and/or active) data warehousing ▪ Understand data warehouse administration and security issues

Opening Vignette Isle of Capri Casinos Is Winning with Enterprise Data Warehouse Company background Problem description Proposed solution Results Answer discuss the case questions Copynight@ 2014 Pearson Education, Inc Slide 2-4

Copyright © 2014 Pearson Education, Inc. Slide 2- 4 Opening Vignette… Isle of Capri Casinos Is Winning with Enterprise Data Warehouse ▪ Company background ▪ Problem description ▪ Proposed solution ▪ Results ▪ Answer & discuss the case questions

Questions for the Opening Vignette 1. Why is it important for Isle to have an EDW? 2. What were the business challenges or opportunities that ? Isle was facing 3. What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development 4. What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case? 5. Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure? Copynight@ 2014 Pearson Education, Inc Slide 2-5

Copyright © 2014 Pearson Education, Inc. Slide 2- 5 Questions for the Opening Vignette 1. Why is it important for Isle to have an EDW? 2. What were the business challenges or opportunities that Isle was facing? 3. What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development. 4. What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case? 5. Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure?

Main Data Warehousing Topics dW definition Characteristics of Dw Data marts ODS. EDW. Metadata DW Framework DW Architecture &EtL Process DW Development DW Issues Copynight@ 2014 Pearson Education, Inc Slide 2-6

Copyright © 2014 Pearson Education, Inc. Slide 2- 6 Main Data Warehousing Topics ▪ DW definition ▪ Characteristics of DW ▪ Data Marts ▪ ODS, EDW, Metadata ▪ DW Framework ▪ DW Architecture & ETL Process ▪ DW Development ▪ DW Issues

What is a Data Warehouse? A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time Copynight@ 2014 Pearson Education, Inc Slide 2-7

Copyright © 2014 Pearson Education, Inc. Slide 2- 7 What is a Data Warehouse? ▪ A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format ▪ “The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time

A Historical Perspective to Data Warehousing Mainframe computers Centralized data storage Big Data analytics Simple data entry Data warehousing was born v Social media analytics √ Routine reporting Inmon, Building the Data Warehouse v Text and Web analytics v Primitive database structures v Kimball, The Data Warehouse Toolkit v Hadoop, MapReduce, NoSQL v Teradata incorporated v EDW architecture design In-memory, in-database 一-1970s 1980s -1990s 20005—20105— v Mini/personal computers(PCs) Exponentially growing data Web data Business applications for PCs Consolidation of DW/BI industry Distributer dbms v Data warehouse appliances emerged v Relational Dbms Business intelligence popularized Teradata ships commercial DB Data mining and predictive modeling Business data warehouse coined pen source software Saas, Paas, Cloud Computing Copynight@ 2014 Pearson Education, Inc Slide 2-8

Copyright © 2014 Pearson Education, Inc. Slide 2- 8 A Historical Perspective to Data Warehousing 1970s 1980s 1990s 2000s 2010s ü Mainframe computers ü Simple data entry ü Routine reporting ü Primitive database structures ü Teradata incorporated ü Mini/personal computers (PCs) ü Business applications for PCs ü Distributer DBMS ü Relational DBMS ü Teradata ships commercial DBs ü Business Data Warehouse coined ü Centralized data storage ü Data warehousing was born ü Inmon, Building the Data Warehouse ü Kimball, The Data Warehouse Toolkit ü EDW architecture design ü Exponentially growing data Web data ü Consolidation of DW/BI industry ü Data warehouse appliances emerged ü Business intelligence popularized ü Data mining and predictive modeling ü Open source software ü SaaS, PaaS, Cloud Computing ü Big Data analytics ü Social media analytics ü Text and Web Analytics ü Hadoop, MapReduce, NoSQL ü In-memory, in-database

Characteristics of Dws Subject oriented Integrated Time-variant (time series) Nonvolatile Summarized Not normalized Metadata Web based, relational/multi-dimensional Client/server. real-time/right-time/active Copynight@ 2014 Pearson Education, Inc Slide 2-9

Copyright © 2014 Pearson Education, Inc. Slide 2- 9 Characteristics of DWs ▪ Subject oriented ▪ Integrated ▪ Time-variant (time series) ▪ Nonvolatile ▪ Summarized ▪ Not normalized ▪ Metadata ▪ Web based, relational/multi-dimensional ▪ Client/server, real-time/right-time/active …

Data Mart A departmental small-scale"DW that stores only limited/relevant data Dependent data mart A subset that is created directly from a data warehouse Independent data mart A small data warehouse designed for a strategic business unit or a department Copynight@ 2014 Pearson Education, Inc Slide 2-10

Copyright © 2014 Pearson Education, Inc. Slide 2- 10 Data Mart A departmental small-scale “DW” that stores only limited/relevant data ▪ Dependent data mart A subset that is created directly from a data warehouse ▪ Independent data mart A small data warehouse designed for a strategic business unit or a department