IMS 6217: Data Warehousing / Business Intelligence 1 Dr. Lawrence West, Management Dept., University of Central Florida [email protected]Database Performance Part 1—Topics • Doing vs. Deciding—OLTP vs. OLAP • Data Warehouses – Fact tables, Dimension tables, Granularity – DW in an integrated Business Intelligence system • Design Steps • Designing Fact Tables • Designing Dimension Tables – The Time dimension • Fact Table Exercises • The AdventureWorks DW
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IMS 6217: Data Warehousing / Business Intelligence 1 Dr. Lawrence West, Management Dept., University of Central Florida [email protected] Database Performance.
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IMS 6217: Data Warehousing / Business Intelligence
1Dr. Lawrence West, Management Dept., University of Central [email protected]
Database Performance Part 1—Topics
• Doing vs. Deciding—OLTP vs. OLAP
• Data Warehouses
– Fact tables, Dimension tables, Granularity
– DW in an integrated Business Intelligence system
• Design Steps
• Designing Fact Tables
• Designing Dimension Tables
– The Time dimension
• Fact Table Exercises
• The AdventureWorks DW
IMS 6217: Data Warehousing / Business Intelligence
2Dr. Lawrence West, Management Dept., University of Central [email protected]
"With uncertainty present…"
With the introduction of uncertainty—the fact of ignorance and necessity of acting upon opinion rather than knowledge—into this Eden-like situation, its character is completely changed. With uncertainty absent, man's energies are devoted altogether to doing things; it is doubtful whether intelligence itself would exist in such a situation; in a world so built that perfect knowledge was theoretically possible, it seems likely that all organic readjustments would become mechanical, all organisms automata. With uncertainty present, doing things, the actual execution of activity, becomes in a real sense a secondary part of life; the primary problem or function is deciding what to do and how to do it. The two most important characteristics of social organization brought about by the fact of uncertainty have already been noticed. In the first place, goods are produced for a market, on the basis of an entirely impersonal prediction of wants, not for the satisfaction of the wants of the producers themselves. The producer takes the responsibility of forecasting the consumers' wants. In the second place, the work of forecasting and at the same time a large part of the technological direction and control of production are still further concentrated upon a very narrow class of the producers, and we meet with a new economic functionary, the entrepreneur. Frank H. Knight
University of Chicago 1921
IMS 6217: Data Warehousing / Business Intelligence
3Dr. Lawrence West, Management Dept., University of Central [email protected]
Doing vs. Deciding
• Organizations do many things
– List thirty transactions that your project organization executes or does
– Start with the Top-Ten list from Projects 2 & 3
• Managers decide things
– List thirty decisions that your project organization makes
– Identify where in the organizational hierarchy the decision lies
– What is the consequence/importance of the decision?
– What information influences each decision?
IMS 6217: Data Warehousing / Business Intelligence
4Dr. Lawrence West, Management Dept., University of Central [email protected]
Doing vs. Deciding / OLTP vs OLAP
• Are systems designed to support the execution of events suitable for the making of decisions?
• Event/transaction support requires – High throughput– High reliability– Accuracy– DB structures tuned for storage & performance
• Online Transaction Processing (OLTP) systems support events– Provide data or information to support transactions– Record acts → New data
IMS 6217: Data Warehousing / Business Intelligence
5Dr. Lawrence West, Management Dept., University of Central [email protected]
OLTP vs. OLAP—Let Me Count the Ways…
• Online Analytical Processing (OLAP) or Business Intelligence (BI) systems are oriented at decision making and analysis
• What are the problems with using our OLTP databases to support managerial decision making?
?
IMS 6217: Data Warehousing / Business Intelligence
6Dr. Lawrence West, Management Dept., University of Central [email protected]
The Data Warehouse
• The DW is a separate storage structure
• Designed to optimize query execution
– Not storage efficiency
– Not transaction throughput
• Expected to be loaded during down times
• Supports "readability"
• May sacrifice details for summaries
• Data and structures anticipate user needs
– Recurring decisions
– Flexible exploration
IMS 6217: Data Warehousing / Business Intelligence
7Dr. Lawrence West, Management Dept., University of Central [email protected]
Steps and Components
• Source Systems—provide raw data to the DW
• Integration Services—Provide transformation and loading services from source data to DW
• Data Warehouse—Customized data store for Business Intelligence
• Analysis Services—Tools for data mining and reporting
• Reporting Services—Our old friend acting on an enhanced data store
IMS 6217: Data Warehousing / Business Intelligence
8Dr. Lawrence West, Management Dept., University of Central [email protected]
Our Approach
• This Week
– Discuss DW storage strategies
– Discuss data to be stored
• Internal data from OLTP systems
• External data
– Design exercises
• Next Week
– DW loading strategies
– DW tools—Analysis Services
IMS 6217: Data Warehousing / Business Intelligence
9Dr. Lawrence West, Management Dept., University of Central [email protected]
Storage Strategies
• The DW stores transformed data that
– May be accessed directly to support analysis
– Supports actions of the Analysis Services to provide enhanced and efficient analysis
• Multiple Strategies
• We will look at the widely used approach using
– Fact tables,
– Dimension tables,
– Arranged in a Star Schema or Snowflake Schema (or both)
IMS 6217: Data Warehousing / Business Intelligence
10Dr. Lawrence West, Management Dept., University of Central [email protected]
Fact Tables Contain Facts (duhhhh) of Interest
• No PK designated for fact table
• Natural PK is TimeKeyOrdered, ProductKey, CustomerKey
IMS 6217: Data Warehousing / Business Intelligence
24Dr. Lawrence West, Management Dept., University of Central [email protected]
Dimensions—Time (cont.)
• The time dimension tablemaps from the measured timeattribute associated with thefact table record to variouslabels and aggregationsassociated with that value
• Facilitates summarizing byvarious aggregates with asingle time dimension measure
• TimeKey PK is often a datetime data type to the date level of precision