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An Introduction to Dimensional Data Warehouse Design

An Introduction to Dimensional Data Warehouse Design. Presented by Joseph J. Sarna Jr. JJS Systems, LLC. Relational Normal Form. Most relational databases are set to 3 rd normal form 1 st Normal form – Tables have unique keys and no repeating groups or multi-value fields

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An Introduction to Dimensional Data Warehouse Design

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  1. An Introduction to Dimensional Data Warehouse Design Presented by Joseph J. Sarna Jr. JJS Systems, LLC

  2. Relational Normal Form • Most relational databases are set to 3rd normal form • 1st Normal form – Tables have unique keys and no repeating groups or multi-value fields • 2nd Normal form – Every attribute is dependent ont the entire key of the table • 3rd Normal form – Attributes are dependent only on the key. No derived elements

  3. Northwind Database Model – Relational Format

  4. Why Dimensional Data Warehouses? • Business needs to analyze data so that it can: • Understand trends • Predict future behavior and needs • Personalize contact with customers • Be competitive • All of this in a speedy manner, with the ability to do “What if’s”

  5. Drawbacks to Relational Data Structures • Data is not structured for analytical usage • Multiple Joins are resource intensive • Missing data from external sources, context history, not operational sources

  6. What Is a Dimensional Data Warehouse? “A structured repository of validated and integrated historical information accessible to business people to provide the basis for both tactical and strategic business decisions.” • Centralized extract and staging • Separate from operational system • Structured for analysis • Historically contexted

  7. Dimensional Data Warehouse Architecture Relational Data External Data Enterprise Data Data Distribution Acquisition, Staging, Cleaning, Transformation Data Warehouse Storage Analytical Applications

  8. Levels of Design • Detail Level • Dimensional Normal form • Value and feasibility • Analytical Level • Structured for the required analyses • Summary Level • Summaries for user requirements • Better response time

  9. Dimensional Normal Form • Normalized for maintainability • De-normalized for performance, based on rules • 2 level structure, therefore only one level of joins required for queries

  10. Northwind Database Model – Dimensional Format

  11. Dimensional Schema • Fact Tables • contain related measures • Usually the largest tables • Usually appended to • Can contain detail or summary data • Measures are usually additive • Dimension Tables • Contain descriptors • Utilize business terminology • Textual and discrete data • Attributes through which the table measures are analyzed

  12. Northwind Database – Star Schema – Orders

  13. Resources • Books • The Data Warehouse Toolkit, Ralph Kimball • The Data Warehouse Lifecycle Toolkit, Ralph Kimball, et al • Data Warehouse Design Solutions, Adamson / Venerable • Websites • http://www.ralphkimball.com/ • http://www.atre.com • http://www.microsoft.com/sql/evaluation/overview/dataware.asp

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