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Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses [Multiple-component retail product]

  • Formaat: Multiple-component retail product, 416 pages, kõrgus x laius: 234x190 mm, kaal: 709 g, Ill., Contains 1 Book and 1 CD-ROM
  • Ilmumisaeg: 16-Feb-1996
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471153370
  • ISBN-13: 9780471153375
Teised raamatud teemal:
Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data WarehousesSuurem pilt
  • Formaat: Multiple-component retail product, 416 pages, kõrgus x laius: 234x190 mm, kaal: 709 g, Ill., Contains 1 Book and 1 CD-ROM
  • Ilmumisaeg: 16-Feb-1996
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471153370
  • ISBN-13: 9780471153375
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471153375.html
Märksõnad:
A leading proponent of the dimensional data warehouse, a database model designed to improve managers' ability to quickly analyze large, multidimensional data sets, provides concrete tools for designing, building, managing, and using dimensional data warehouses for different types of business applications. A CD- ROM includes software for querying dimensional data warehouses and working models of all databases described in the book. Annotation c. by Book News, Inc., Portland, Or.
Introduction xv
Dimensional Modeling xviii
Why Model? xviii
Snatching Defeat from the Jaws of Victory xxi
The Goals of a Data Warehouse xxiii
The Goals of This Book xxv
Two Different Worlds
1(20)
Consistency
2(1)
What is a Transaction?
3(1)
Users and Managers
3(2)
One Machine or Two
5(2)
The Time Dimension
7(1)
The Entity Relation Data Model
8(2)
The Dimensional Model
10(2)
The Fact Table
12(1)
The Dimension Tables
13(1)
The Standard Template Query
14(3)
Attributes Are the Drivers of the Data Warehouse
17(1)
Spontaneous Evolution
18(1)
A Word About OLAP
19(2)
The Grocery Store
21(28)
Steps in the Design Process
22(1)
Grocery Store Item Movement
22(3)
Identifying the Processes to Model
25(2)
Picking the Business Measurements for the Fact Table
27(2)
Resisting Normalization
29(1)
Preserving Browsing
30(2)
The Time Dimension
32(3)
The Product Dimension
35(4)
The Store Dimension
39(2)
The Promotion Dimension
41(3)
The Grocery Store Facts
44(2)
Database Sizing for the Grocery Chain
46(3)
The Warehouse
49(16)
Inventory Levels: Another Semiadditive Fact
49(1)
Inventory Models
50(1)
The Inventory Snapshot Model
51(2)
Gross Margin Return on Inventory (GMROI)
53(1)
The Delivery Status Model
54(5)
The Transaction Model
59(3)
Database Sizing for Inventory Snapshot
62(1)
Database Sizing for Inventory Delivery Status
62(1)
Database Sizing for Inventory Transaction
62(3)
Shipments: The Most Powerful Database
65(16)
The Most Powerful Database
66(2)
The Ship-to Dimension
68(2)
The Deal Dimension
70(2)
The Ship-from Dimension
72(1)
The Ship Mode Dimension
72(1)
Profit and Loss (P&L)
73(4)
Customer Satisfaction
77(1)
The Invoice Number: A Degenerate Dimension
78(1)
Database Sizing for Shipments
79(2)
The Value Chain
81(8)
The Demand Value Chain
81(4)
Subdivisions Within the Value Chain
85(1)
The Supply Value Chain
86(3)
The Big Dimensions
89(18)
The Product Dimension
89(3)
The Merchandise Hierarchy
92(1)
The True Meaning of Drill Down
93(1)
Multiple Hierarchies
94(1)
Resisting the Urge to Snowflake
95(1)
The Threat to Browsing Performance
95(2)
Really Big Customer Dimensions
97(1)
Demographic Minidimensions
98(2)
Slowly Changing Dimensions
100(5)
Slowly Changing Minidimensions
105(2)
Financial Services, Especially Banks
107(10)
Dirty Dimensions
109(2)
Semiadditive Account Balances
111(1)
Heterogeneous Products
112(3)
Interaction with Transaction Grain Fact Tables
115(1)
Using Big-Dimension Techniques
116(1)
Database Sizing for Household Banking
116(1)
Subscription Businesses
117(8)
Subscription Transactions
117(3)
Payments in Advance
120(3)
Database Sizing for Cable TV Sales Transactions
123(1)
Database Sizing for Cable TV Sales Monthly Summary
124(1)
Insurance
125(18)
Policy Creation
126(3)
Claims Processing
129(4)
Monthly Snapshots for Policies and Claims
133(1)
Transaction Schemas with Heterogeneous Products
134(3)
Snapshot Schemas with Heterogeneous Products
137(1)
Minidimensions in Insured Party and Covered Item
138(2)
Design Summary
140(1)
Database Sizing for Insurance Policy Transactions
140(1)
Database Sizing for Claim Transactions
141(1)
Database Sizing for Core Policy Snapshot
141(1)
Database Sizing for Claims Snapshot
141(2)
Factless Fact Tables
143(10)
Event Tracking Tables
143(2)
More Event Examples
145(2)
Coverage Tables
147(2)
Database Sizing for College Course Factless Fact Table
149(1)
Database Sizing for Hospital Patient Procedure Factless Fact Table
150(1)
Database Sizing for Accident Parties Factless Fact Table
150(1)
Database Sizing for Promotion Coverage Factless Fact Table
150(1)
Database Sizing for Big Product Sales Factless Fact Table
151(2)
Voyage Businesses
153(8)
The Frequent Flyer Data Warehouse
153(2)
Shipping Data Warehouse
155(2)
Travel Credit Card Data Warehouse
157(1)
Database Sizing for Airline Frequent Flyer Fact Table
158(1)
Database Sizing for Shipper Fact Table
159(1)
Database Sizing for Hotel Stays Fact Table
159(1)
Database Sizing for Car Rentals Fact Table
160(1)
Building a Dimensional Data Warehouse
161(26)
The Nine Decision Points
162(4)
Interviewing the End Users
166(2)
The Content of the End User Interviews
168(9)
Interviewing the DBAs
177(2)
The Nine Decisions Revisited
179(1)
Assembling the Team
180(1)
Filling in the Details of the Tables
181(1)
The Top-Level Physical Design
181(1)
How to Choose Hardware and Software
182(5)
Aggregates
187(24)
Database Sizing
187(3)
Advantages of Aggregates
190(1)
How to Store Aggregates
191(8)
Sparsity Failure and the Explosion of Aggregates
199(3)
Indexing Issues
202(1)
Administration Issues
203(1)
Application Issues
204(1)
Aggregate Navigation
205(2)
Incremental Rollout of Aggregations
207(1)
The Aggregate Navigation Strategy
208(1)
Aggregations Provide a Home for Planning Data
209(2)
The Back Room
211(20)
The Daily Rhythms: Querying and Loading
211(1)
The Query Phase
212(1)
Browse Queries
213(1)
Multitable Join Queries
214(1)
The Loading Phase
215(1)
The Production Data Extract System
216(10)
Conforming Dimensions
226(2)
New Is Back Room Roles
228(3)
The Front Room
231(12)
Query Tools as Clients
231(3)
Query Loads in Dimensional Data Warehouse Environments
234(1)
Completing the Application
235(7)
Handing Off the Data
242(1)
Keeping the Network Running Fast
242(1)
Front End Applications
243(36)
The Internal Architecture of a Query Tool
243(1)
Stitching Together Multiple Answer Sets
244(8)
On-the-Fly DBMS Tables
252(1)
User Interface Recommendations
253(4)
Query Tool Features
257(17)
Administrative Responsibilities
274(5)
The Future
279(10)
Software Is the Key
279(1)
Optimizing the Execution Strategy for Star Join Queries
280(2)
Indexing of Dimension Tables
282(1)
Fast Substring Search
283(1)
Geographic Indexes
283(1)
Data Compression
283(1)
Parallel Processing
284(1)
Smarter Caching
285(1)
Syntax Extensions for SQL
286(1)
Event Tracking
287(1)
Administrative Tools
287(2)
Appendix A. Design Principles for a Dimensional Data Warehouse 289(12)
Appendix B. A System Checklist for a Perfect Dimensional Data Warehouse 301(6)
Appendix C. A Glossary for a Dimensional Data Warehouse 307(14)
Appendix D. User's Guide for Star Tracker™ 321(46)
Overview
321(1)
About the User's Guide
322(1)
Start Tracker Basics
323(3)
Star Tracker Main Window
326(19)
Browsing and Defining Groups
345(10)
Building a New Report
355(10)
Other Features
365(2)
Index 367