Presentation is loading. Please wait.

Presentation is loading. Please wait.

Modeling the Data Warehouse Chapter 7. Data Warehouse Database Design Phases zDefining the business model (conceptual model) zCreating the dimensional.

Similar presentations


Presentation on theme: "Modeling the Data Warehouse Chapter 7. Data Warehouse Database Design Phases zDefining the business model (conceptual model) zCreating the dimensional."— Presentation transcript:

1 Modeling the Data Warehouse Chapter 7

2 Data Warehouse Database Design Phases zDefining the business model (conceptual model) zCreating the dimensional model (logical model) zModeling summaries zCreating the physical model Select a business process Physical model 2,3

3 Performing Strategic Analysis Phase 1: Defining the Business Model Select a business process Performing strategic analysis Creating the business (conceptual) model

4 Creating the Business Model Phase 1: Defining the Business Model Performing strategic analysis ’ Creating the business (conceptual)model - Defining business requirements - Identifying the business measures - Identifying the dimensions - Identifying the grain - Identifying the business definitions and rules - Verifying data sources

5 Phase 1: Defining the Business Model Creating the Business Model Performing strategic analysis Creating the business (conceptual) model - Defining business requirements - Identifying the business measures - Identifying the dimensions - Identifying the grain - Identifying the business definitions and rules - Verifying data sources

6 Business Requirements Drive the Design Process Primary input Other inputs Business requirements Existing metadata Production ERD model ResearchNonrelational legacy systems

7 Identifying Measures and Dimensions The attribute varies continuously: Balance United Sold Cost Sales The attribute is perceived as a constant or discrete value: Description Location Color Size DimensionsMeasures

8 Determining Granularity YEAR? QUARTER? MONTH? WEEK? DAY?

9 Identifying Business Rules Location Geographic proximity 0 - 1 miles 1 - 5 miles > 5 miles Product Type Monitor Status PC 15 inch New Server 17 inch Rebuilt 19 inch Custom None Time Month>Quarter>Year Store Store>District>Region

10 Creating the Dimensional Model Identify fact tables - Translate business measures into fact tables - Analyze source system information for additional measures - Identify base and derived measures - Document additivity of measures Identify dimension tables Link fact tables to the dimension tables Create views for users

11 Dimension Tables Dimension tables have the following characteristics: zContain textual information that represents the attributes of the business zContain relatively static data zAre joined to a fact table through a foreign key reference Facts (units, price) Channel Time Product Customer

12 Fact Tables Fact tables have the following characteristics: zContain numeric measures (metric) of the business zMay contain summarized (aggregated) data zMay contain date-stamped data zAre typically additive zHave key value that is typically a concatenated key composed of the primary keys of the dimensions zJoined to dimension tables through foreign keys that reference primary keys in the dimension tables

13 Facts (units, price) Channel Time Product Customer Fact table Dimension tables

14 Star Schema Model zCentral fact table zRadiating dimensions zDenormalized model Sales Fact Table Product_id Store_ id Item_id Day_id Sales_dollars Sales_units Product Table Product_id Product_desc Store Table Store_id District_id Time Table Day_id Month_id Year_id Item Table Item_id Item_desc

15 Star Schema Model zEasy for users to understand zFast response to queries zSimple metadata zSupported by many front end tools zLess robust to change zSlower to build zDoes not support history

16 Snowflake Schema Model Sales Fact Table Product_id Store_ id Item_id Day_id Sales_dollars Sales_units Product Table Product_id Product_desc Store Table Store_id District_id Time Table Day_id Month_id Year_id Item Table Item_id Item_desc District Table District_id District_desc Dept Table Dept_id Dept_desc Mgr_id Mgr Table Dept_id Mgr_id Mgr_name

17 Snowflake Schema Model zDirect use by some tools zMore flexible to change zProvides for speedier data loading zMay become large and unmanageable zDegrades query performance zMore complex metadata CountryStateCountyCity

18 Using Summary Data Phase 3: Modeling summaries zProvides fast access to precomputed data zReduces use of I/O, CPU, and memory zIs distilled from source systems and precalculated summaries zUsually exists in summary fact tables

19 Designing Summary Tables zAverage zMaximum zTotal zPercentage UnitsSales($)Store Product A Total Product B Total Product C Total

20 Summary Tables Example SALES FACTS Sales$ Region Month 10,000 North Jan 99 12,000 North Feb 99 11,000 South Jan 99 15,000 West Mar 99 18,000 South Feb 99 20,000 North Jan 99 10,000 East Jan 99 2,000 West Mar 99 SALES BY MONTH/REGION Month Region Tot_Sales$ Jan 99 North 41,000 Jan 99 East 10,000 Feb 99 South 40,000 Mar 99 West 17,000 SALES BY_MONTH Month Tot_Sales Jan 99 51,000 Feb 99 40,000 Mar 99 17,000

21 Summary Management in Oracle8i Summary advisor Summary recommendations Space requirements Summary usage Region State City ProductTime Sales summary

22 Using Time in the Data Warehouse

23 The Time Dimension zTime is critical to the data warehouse zA consistent representation of time is required for extensibility Sales fact Time dimension Where should the element of time be stored?

24 Creating the Physical Model Phase 4: Creating the Physical Model Translate the dimensional design to a physical model for implementation Define storage strategy for tables and indexes Perform database sizing Define initial indexing strategy Define partitioning strategy Update metadata document with physical information

25 Physical Model Design Tasks zDefine naming and database standards zPerform database sizing zDesign tablespaces zDevelop initial indexing strategy zDevelop data partition strategy zDefine storage parameters zSet initialization parameters zUse parallel processing

26 Using Data Modeling Tools zTools with a GUI enable definition, modeling, and reporting zAvoid a mix of modeling techniques caused by: - Development pressure - Developers with lack of knowledge - No strategy zDetermine a strategy zWrite and publish formally zMake available electronically Spreadsheets CASE tools Paper and pencil

27 Summary This lesson discussed the following topics: zCreating a business model zCreating a dimensional model zModeling the summaries zCreating a physical model Select among business processes Physical model 2,3 Business model Dimensional model


Download ppt "Modeling the Data Warehouse Chapter 7. Data Warehouse Database Design Phases zDefining the business model (conceptual model) zCreating the dimensional."

Similar presentations


Ads by Google