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Datawarehousing_Final_ProjectPresentation
This document summarizes a presentation about analyzing hockey statistics from various teams and players over multiple years. It discusses designing dimension and fact tables to store the data, extracting and transforming the raw data using SSIS and SSAS, and answering business questions by demonstrating OLAP on metrics like power play conversion percentages and physical capacity. The presentation concludes with a Q&A section.
Datawarehousing_Final_ProjectPresentation
- 1. GROUP-3 HOCKEY-PHILES Dec 2,2014 CARNEGIE MELLON UNIVERSITY TeamMembers: Alohali, Abdulaziz Challa, Sneha Dai, Daisy Gajwani, Mahesh Hou, Rebecca
- 2. How much doyou know about Hockey? Hockey What’s the name of the famous Hockey Team in Pittsburgh? During which year did that team perform at its best between 1967-2011? Who Is the best player in that team between 2006-2011 during regular season? What about other teams?
- 3. Agenda • Introduction • Design •ETL • Business Questions • OLAP demo • Conclusion • Q&A
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- 5. Design Fact –Dim Visiograph ◦ Dim ◦ Fact (why 2 fact) Challenges Number of records SCD Type 2 Grain
- 6. DesignBig dataset only choose the relevant attributes.
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- 9. ETL Tools: •SSIS (SQL SERVERINTEGRATION SERVICE) •SSAS (SQL SERVER ANALYSIS SERVICE) Extraction Load Transformation dataset staging table Cleansing data, adding extra records,& date conversion Load data annually
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- 13. POWER PLAY CONVERSIONPERCENTAGE
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Editor's Notes
- #3 No clue about the topic at all. Ask question #1 that will lead to the topic. Ask Q#2 ( something about the pinguen team) We found a dataset for the hocky statistics since 1904. we thought it would be a good suit for our DW project. So that is this dataset about ? Next slide (Design)
- #5 Our goal is to analyze the performance of ice-hockey players and teams from different Hockey Leagues from the years 1901 to 2011 during the regular season period.The data set has details about statistics of the performance of players and teams related to five Hockey Leagues and the teams from those leagues durng regular season. Detailed measurement include goals, Winning percentage of the teams, Number of Power Play Goals, Shootout wins etc. The analysis will help better understand the history of Hockey Leagues, the evolution of Hockey in the eastern and western regions of the United Staes and may be useful to discover potential promising Hockey players of the future in the United States and other places. Our datawarehouse also helps capture specific information on the evolution of Pittsburgh Penguins team over the aforementioned period.
- #12 E: dataset staging table Data mart Tools we use T: cleansing the data was to fill the empty cells, adding extra records , and date conversion (time dimension, add period, century information) L: load data annually