Intro to EDW
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The document discusses the key aspects of an enterprise data warehouse (EDW) including data modeling, extracting data from source systems using ETL processes, building cubes in Analysis Services for analytics, and reporting on the data using SQL Server Reporting Services and Excel pivot tables. It provides an overview of the different roles and technologies involved in an EDW as well as examples of dimensional modeling techniques.
Intro to EDW
- 1. Presented by: Jonathan Bloom Senior BI Consultant, Agile Bay, Inc.
- 2. Customers & Partners Jonathan Bloom Current Position: Senior BI Consultant Blog: http://www.BloomConsultingBI.com Twitter: @SQLJon Linked-in: http://www.linkedin.com/BloomConsultintBI Email: JBloom@agilebay.com
- 3. w w w . a g i l e b a y. c o m
- 4. Agenda Why EDW Flow of Data Pros and Cons Q&A
- 5. Why EDW?
- 6. Convert Data to Information Accumulating Data Manage the Business OLTP != Reporting Apply Business Rules Clean Data Analytics Distribution
- 7. EDW Role Reporting Lifecycle Domain Knowledge Interact with Business Gather Specs Estimate Time Knowledge of Database SQL Skills Change Management
- 8. Requirements Access to Database Valid Credentials Location of Tables Business Intelligence Dev Studio (BIDS) 2008 R2 SQL Server Data Tools 2012 SQL Analysis Cubes
- 9. EDW Architecture Source System Staging Raw Master Data Services Enterprise Data Warehouse Analysis Services Cubes
- 10. Data Modeling Kimball Methodology Star Schema Pattern forms a graphical “Star” Snow Flake Schema Branches
- 11. Tables Dimension Tables Describe Data Fact Tables Measures (Sums, Counts, Max, Min, etc.) Contain Surrogate Keys Link back to Dim Tables
- 12. SK Keys Surrogate Keys Substitution for the natural primary key Unique Identifier Auto Integer, Indexed, Primary Foreign Key in other tables
- 13. AK Keys Alternate Keys Unique Identifier from Source System Typically Varchar
- 14. Staging Create Tables Dim Tables Fact Tables Use SQL Statements Pull Data Truncate and Reload SSIS = ETL
- 15. DW Create Tables Dim Tables SK / AK Fact Tables SK Use SQL Statements Pull Data SSIS = ETL Time Stamps
- 16. Slowly Changing Dimensions Type 0 method is passive Values remain as they were at the time the dimension record was first inserted Type 1 Overwrites old with new data Does not track historical data Type 2 Tracks historical data by creating multiple records
- 17. Null Values -1 Row Null Vales in Fact Tables = -1
- 18. Date Dimensions Create Scripts Fiscal Year Custom Start / End Dates Key Example: 20140226
- 19. Date Dimensions
- 20. Dim Tables
- 22. Fact Tables
- 24. Fact Table Keys
- 25. Fact Table Keys
- 26. Analysis Server
- 29. SSAS Cubes
- 30. SSAS Create Connections Add Data Sources Create Relationships Add Dimensions Create Measure Groups / Measures Create Calculated Measures Create Hierarchy’s
- 31. Visual Studio
- 32. Create SSRS Report with SSAS Cube
- 33. SSRS SSAS (MDX) Demo: Open SDT Connect to Cube Select Measures Add Fields to Report Run Report Deploy Report
- 34. SSRS SSAS (MDX) Demo:
- 35. SSRS SSAS (MDX) Demo:
- 36. Connect to SSAS Cube via Excel
- 37. Excel – Cube Demo: Open Excel Create Connect
- 38. Excel – Cube Demo:
- 39. Excel – Cube Demo:
- 40. Excel – Cube Demo:
- 42. Summary Learn EDW Data Modeling ETL – SSIS Cubes – SSAS Reports – SSRS / Excel Pivot Tables Stay Current Never stop learning!