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Building a Data Warehouse with SQL Server

Building a Data Warehouse with SQL Server

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Building a Data Warehouse with SQL Server

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  1. Building a Data Warehouse with SQL Server Presented by John Sterrett

  2. About Me…

  3. AITP - Pittsburgh

  4. What is a data warehouse? Building a Data Warehouse with SQL Server

  5. What is Business Intelligence? According to Wikipedia BI refers to skills, technologies, applications and practices used to help a business acquire a better understanding of its commercial context. The following is a definition from Business Intelligence with Microsoft Office PerformancePoint Server 2007 Business intelligence (BI) is more of a concept than a single technology. The goal is to gain insight into the business by bringing together data, formatting it in a way that enables better analysis, and then providing tools that give users power—not just to examine and explore the data, but to quickly understand it.

  6. Data Warehouse = Business Intelligence

  7. Business Problems that BIAddresses Slow-to-execute queries General system slowdowns Manual query writing Disparate data sources Data is not available to all users Too much data

  8. What is BI to the users? BI is fast to query. BI improves your return on investment (ROI) BI is a tool that allows users to find what they need.

  9. Business Cases A technology department could analyze work completed for departments and/or clients. This could then be used to estimate future flat fee engagements, establish seasonal hiring, balance overtime. A medical center could use statistics covering a wide range of lab values and a large number of patients to identify whether a patient currently being treated might be at an elevated risk for a certain types of disease. Application that’s great for data entry but lacks in depth reporting and drilldown capabilities. A company who wants to centralize data to a single data source for allowing decision makers the ability to make decisions as needed.

  10. What Products Are Involved? Database Technologies SQL Server 2005/2008 (Database Engine) SQL Server Analysis Services (SSAS) SQL Server Integration Services (SSIS) User Interface Technologies SQL Server Reporting Services (SSRS) SQL Server Management Studio (SSMS) Performance Point 2007 SharePoint Excel

  11. DEMO! Browse a Cube using Management Studio

  12. Goals for dimensional modeling Understand star schema Understand dimensional modeling Understand changing dimensions Understanding fact (or measure) and cube modeling

  13. Star Schema… A star schemaconsists of at least one fact table and a number of dimension tables. Star Schema is highly recommended schema for SSAS cubes.

  14. Star Schema Example

  15. Facts and Measures Fact table consists of at least two types of data: keys and measures. Keys are usually surrogate keys that link to the dimension tables. Measuresare numeric values that are usually additive that express business metrics.

  16. Dimensions Dimensions describe who, what, when, where and why for the facts. Dimensions should consist of the following data types Surrogate key. Primary key of the loaded source(s) Any additional attributes (columns) that describe the business entity.

  17. Dimension Examples

  18. Slowly Changing Dimensions (SCD)

  19. Hierarchies Hierarchies serve two purposes: Convenience for end users. Provides drill down / drill up features

  20. Where do I go from here? Create Use Grain Statements What are the key metrics for your business? What factors do you use to evaluate those key metrics? What level of granularity do you use evaluate each factor?

  21. Grain Statement Examples: We want to see time worked, hours billed, and cost of work by date, by employee, by department, by location, and by projects. We want to see sales amount and sales quantity by day, by product, by employee, and by store location. We want to see average score and quantity of courses taken, by course, by day, by student, by manager, by curriculum, and by curriculum type.

  22. DEMO Build a Cube

  23. Extracting & Loading Data *The process of extracting, transferring and loading data consumes about 75% of the Data Warehouse project. It is highly recommended to use SSIS for ETL instead of native T-SQL

  24. Basic Dimension Package

  25. Basic Fact Package

  26. So… You are interested in building a Data Warehouse Click here to download a Virtual PC that includes sample Data Warehouses and all of Microsoft’s BI tools. If you already have SQL Server 2005 and Analysis Services configured click here to download samples (Click here for SQL Server 2008) Check out this Introduction to Data Warehousing with SQL Server

  27. Q/A Blog: LinkedIn: Twitter: E-Mail:

  28. References Foundation of SQL Server 2005 Business Intelligence. Business Intelligence with Microsoft Office PerformancePoint Server 2007 ACM – Intro to Data Warehousing