Introduction Data warehouse
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An introduction to Data Warehousing, Fact Tables, Dimensions, Measures, Start Schema and Snow Flake. Benefits of Data Warehouse and Design Approaches.
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Introduction Data warehouse
- 2. What is a Data Warehouse? A Simple Relational Database Different Architecture Less Normalized Analytical Design Facts and Dimensions Non-Operational
- 3. Why Data Warehouse? Operational Systems Transactional Ssytems Legacy Applications Internal / External Feeds Management Reports Dashboards Analytics
- 4. Why Data Warehouse? Management Reports Dashboards Analytics Data Warehouse Operational Systems Transactional Ssytems Legacy Applications Internal / External Feeds
- 5. Benefits of a Data Warehouse Centralized Data Source Enhanced Business Intelligence Increased Query and System Performance Business Intelligence from Multiple Sources Timely Access to Data Enhanced Data Quality and Consistency Historical Intelligence High Return on Investment
- 6. OLTP vs.OLAP OnlineTransaction Processing Optimized forTransactions Concurrent Operations Consistent andAccurate Real-Time Data Short life span Too Many SmallTables Normalized Online Analytical Processing Optimized For Analysis LargeAmounts of Historical Data Fed From OLTP Databases Less Normalized (2NF) Facts and Dimensions Not Real-Time ExtractTransform and Load (ETL)
- 7. Relational vs. Multidimensional DWs Relational DWs Similar to OLTP Simpler Structure Query using SQL Less ProcessingCost Easier Maintenance Best for Real-TimeAd-hoc Reporting Multidimensional DWs Different Structure (Cubes) Different Query Language (MDX) Much Faster for Extra-Large DataSets Pre-Calculated Measures, KPIs Optimized to write and answer complicated requests
- 8. What’s inside a Data Warehouse?
- 9. Inside of Data Warehouse Dimensions Measures Facts
- 10. Dimensions Describing Information Slicing and Dicing Comparing Make the data meaningful Example: Customer Time Product Project Types Categories Colors, Sizes, etc…
- 12. Metrics / Measures Measurable Columns Things we’re actually looking for They are usually aggregated (Sum, Avg, Min, Max…) Examples: Sales Amount Order Quantity Customer Count Tax Paid Etc.
- 13. Facts Describing Measures by Dimensions Tables Containing Multiple Dimension Keys and MeasureValues Usually PrimaryKey is all the Dimensions Keys or the Event Key Dimension Keys are also ForeignKey to DimensionTables Facts usually express real events that happened at a specific time Example: We Sold 2 Toyotas to John Smith in New York Yesterday for $20,000.00 each and gave him $2,000.00 overall discount. So it was totally $38,000.00
- 14. Facts Describing Measures by Dimensions Tables Containing Multiple Dimension Keys and MeasureValues Usually PrimaryKey is all the Dimensions Keys or the Event Key Dimension Keys are also ForeignKey to DimensionTables Facts usually express real events that happened at a specific time Example: We Sold 2 Toyotas to John Smith in New York Yesterday for $20,000.00 each and gave him $2,000.00 overall discount. So it was totally $38,000.00
- 15. Sample Fact Table FKs to DimensionTables Describing the Fact (Used for Drill-Down) Measures
- 16. Fact / Dimension Relationship Star Schema Facts connect DIRECTLY to each Dimension with a single relation. Simple Structure EasierTo Query Not the best approach for complicated Dimensions No Built-in Drill-Down Snow Flake Schema / Dimensions Dimensions are HIERARCHICALLY connected to each other. Facts connect to one of the Dimensions and uses the other ones through the connected dimension. More Complicated Built-in Drill-Down
- 17. StarSchema Fact Dimension 1 Dimension 5 Dimension 4 Dimension 3 Dimension 2
- 20. Designing Data Warehouse Fact Oriented You have and know the business facts Design Facts, fill it with measures and then Dimensions Might need a couple of Iterations. You will end up with Facts with real PrimaryKeys Ex. Internet Sales (we talked about it before) Measure Group Oriented You only know/want your Measures Write down all business measures you need Connect them to Dimensions Group them by their meaning and common Dimensions You will end up with Facts with Dimension Combined Primarykeys Ex. Employee Offdays (TimeKey, EmployeeKey, ReasonKey, OffDayCount)
- 23. About Me Amin Choroomi CTO & Co-Founder at vdash Software Developer, Teacher and Consultant DataVisualization, Analytics, Dashboards Data Warehousing, Integration, Business Intelligence http://www.vdash.ir [email protected] [email protected] https://linkedin.com/in/choroomi @aminchoroomi
- 24. ThankYou