1. DW-1: Introduction to Data Warehousing

2. Overview What is Database What Is Data Warehousing Data Marts and Data Warehouses The Data Warehousing Process Data in a Data Warehouse

3.  What Is Database Before Program = Algorithm + Data Structure Now Application (Weblication) = Visual I/F + SQL Query + Database Database is Integrated Data from multiple file system data for OLTP Data Base (From Air Base?), DB, 데이타베이스, 자료기지 ( 북한 )

4.  Database and Data Model Computer Representation of Data for efficient understanding and processing Data Model based on Relationship modeling Relationship between record one-to-one(1:1), one-to-many(1:N), many-to-many(N:M) Hierarhical Model: Hierarchical Relationship, 1:N Network model: Network like relationship, N:M Relational Model: Use relation (table) for Relationship Object-Oriented data model: Complex object modeling SET type, Reference, List

5.  What Is Data Warehousing Defining Data Warehousing Operational Systems: A Transactional Solution Analytical Systems: A Data Warehousing Solution Comparing Transactional and Data Warehousing Solutions

6. Defining Data Warehousing Business Intelligence Database Marketing: Personalized Product Especially S/W, Cocoon business etc. Electronic Commerce Data Warehouse: 자료 창고 for OLAP, Data Mining, DSS Knowledge Management Data Warehousing: Process to build Data Warehouse

7. Defining Data Warehousing A Data Warehouse Is a Database That Contains: Enterprise data Integrated sets of historical data Subject-oriented, consolidated, consistent data Data structured for distribution and querying A Data Warehousing Solution Is a Process That: Retrieves and transforms data Manages the database Uses tools for building and managing the data warehouse

8. Operational Systems: A Transactional Solution Track Individual Events Used for Real-time Data Entry and Editing Examples: Order-tracking applications Customer service applications Point-of-sale applications Service-based sales applications Banking functions

9. Analytical Systems: A Data Warehousing Solution Assist with Strategic Decision Support Provide Different Levels of Analysis Allow Users to Navigate to Different Levels of Data Allow System Searches to Find New Relationships Examples: Spreadsheet-based applications Sales forecasting applications

10. Comparing Transactional and Data Warehousing SolutionsTransactionalsolutionsTransactionalsolutions Data warehousing solutions solutions Update frequency Real-time Periodically Structured for Data integrity Ease in querying Optimized for Transaction performance Query performance

11.  Data Marts and Data Warehouses What Is a Data Mart Moving Data from a Data Warehouse to Data Marts Moving Data from Data Marts to a Data Warehouse

12. What Is a Data Mart A subset of a data warehouse Used in an enterprise Specific to a particular subject or business activity Why Build Data Marts Faster queries and fewer users Faster deployment time Integrated Data Marts Ensure consistent data Require advance planning

13. Moving Data From a Data Warehouse to Data Marts Advantages Shared fields Common source Distributed processing Disadvantages Longer time to develop Customer Service Mart Sales Mart DataWarehouse Financial Mart Source 1 Source 2 Source 3

14. Moving Data from Data Marts to a Data Warehouse Advantages Simpler and faster to implement Department-specific data Smaller hardware requirements Disadvantages Data duplication Incompatible data marts DataWarehouse Sales Mart Financial Mart Customer Service Mart Source 1 Source 2 Source 3

15.  The Data Warehousing Process Basic Elements of the Process Tools to Manage the Process

16. Basic Elements of the Process Data MartsDataWarehouse Source OLTP Systems Clients Retrieve Data Populate Populate Query Transform Data Data Warehouse Data Marts the Data Retrieve Data Populate Populate Query Transform Data Data Warehouse Data Marts the Data 1 2 345

17. Tools to Manage the Process SQL Server Data Transformation Services SQL Server OLAP Services Microsoft Repository Microsoft English Query PivotTable Service

18. ETL process Extraction, Transformation, Loading Extraction: 추출 Data retrieval from existing data source such as File, Table etc. Transformation: 변환 Data modification, sorting, calculation etc Loading: 적재 Bulk, incremental loading from operational DB Time consuming process: may use special H/W

19.  Data in a Data Warehouse Data Characteristics Example of Organizing Data

20. Data Characteristics Data characteristic DescriptionDescription Consolidated Enterprise-wide Consistent Within the data warehouse Subject-oriented Organized to user perspective Historical Snapshots over time Read-only Cannot update Summarized To appropriate level of detail

21. Example of Organizing Data Southeast Region Total City Miami Tampa Atlanta Savannah Columbia Monthly Southeast Regional Sales Report - May 1999 State FL FL Totals GA GA Totals SC SC Totals Units Sold 2,500 2,750 5,250 3,200 1,725 4,925 1,900 12,075 Sales $ $12,850 $14,135 $26,985 $16,800 $ 9,143 $25,943 $ 9,595 $62,473

22. Data Warehouse Schema Example: Star schema

23. A Example of Cube Browsing 1 Fact with 4 Dimension Table -- Sales_Fact, Product, Store, Time, Customer

24. Drilling Down Drilling Down to products

25. Drilling Down Drilling Down to the lowest level of Customer Dimension

26. Rolling up

27. Review What Is Data Warehousing Data Marts and Data Warehouses The Data Warehousing Process Data in a Data Warehouse Data Warehouse will be more popular than DB?