Week end Before April 9th is the day to run through the presentation with Feedback for the GOMC !!!
Profile it and clean up. AYFG operational database and put in into SSIS
Build a data mart - OLAP, Visualize and Dashboard
Network Analysis - Advanced Analysis
Del - Dashboards and Network Analysis reports
Going to submit the video
May 7th is the deliverable date !
OLAP Reporting on Monday
SSIS
Dimensional Modelling
MON -> MS BI Suite OLAP Demo
WEd -> OBIE Suite Demo
Difference of working of the OBIE and MSBI tool. Create a data-mart
Next HW : Analysis of the airline Db (Tean)
Next HW : Info-graphic resume (Indi)
Next HW : Learnings of the class(Team)
Social Media Analytics
Facts for AYFG :
Quantity
Items
Time
Members
Gym
Customers
Store
Granularity : Specific Item across a particular gym at particular time. You are able to aggregate it across multiple facts.
Dimensions act as a mechanism to describe individual fact.
What happens when promotions are going on ? What if more promotions are going on simultaneously
You can have promotions as a dimensions. All products might not have dimensions
Promotion Dimension Table
PSK
Name
Details
Can be a simple price discount. Buy 1 Get 1 free !
You might not want to have a separate table.
Factless Fact Table
ISK -> Item
SSK - Store
TSK -> Time
PSK -> Promotion
Set difference will tell you what was on sale and did not sell or the other way.
A factless fact table is a table that contains nothing but dimensional keys.
There are two types of factless tables. One is for capturing the event. An event establishes the relationship among the dimension members from various dimension but there is no measured value. The existence of the relationship itself is the fact.
This type of fact table itself can be used to generate the useful reports. You can count the number of occurrences with various criteria. For example, you can have a factless fact table to capture the student attendance (the example used by Ralph). The following questions can be answered:
All the queries are based on the COUNT() with the GROUP BY queries. I think that the interesting metrics are the nested GROUP BY so you can first count and then apply other aggregate functions such as AVERAGE, MAX, MIX.
The other type of factless table is called Coverage table by Ralph. It is used to support negative analysis report. For example a Store that did not sell a product for a given period. To produce such report, you need to have a fact table to capture all the possible combinations. You can then figure out what is missing.
No tuple appears if there is no sale/purchase. You can have NULL values for surrogate keys.
Profile it and clean up. AYFG operational database and put in into SSIS
Build a data mart - OLAP, Visualize and Dashboard
Network Analysis - Advanced Analysis
Del - Dashboards and Network Analysis reports
Going to submit the video
May 7th is the deliverable date !
OLAP Reporting on Monday
SSIS
Dimensional Modelling
MON -> MS BI Suite OLAP Demo
WEd -> OBIE Suite Demo
Difference of working of the OBIE and MSBI tool. Create a data-mart
Next HW : Analysis of the airline Db (Tean)
Next HW : Info-graphic resume (Indi)
Next HW : Learnings of the class(Team)
Social Media Analytics
Facts for AYFG :
Quantity
Items
Time
Members
Gym
Customers
Store
Granularity : Specific Item across a particular gym at particular time. You are able to aggregate it across multiple facts.
Dimensions act as a mechanism to describe individual fact.
What happens when promotions are going on ? What if more promotions are going on simultaneously
You can have promotions as a dimensions. All products might not have dimensions
Promotion Dimension Table
PSK
Name
Details
Can be a simple price discount. Buy 1 Get 1 free !
You might not want to have a separate table.
Factless Fact Table
ISK -> Item
SSK - Store
TSK -> Time
PSK -> Promotion
Set difference will tell you what was on sale and did not sell or the other way.
A factless fact table is a table that contains nothing but dimensional keys.
There are two types of factless tables. One is for capturing the event. An event establishes the relationship among the dimension members from various dimension but there is no measured value. The existence of the relationship itself is the fact.
This type of fact table itself can be used to generate the useful reports. You can count the number of occurrences with various criteria. For example, you can have a factless fact table to capture the student attendance (the example used by Ralph). The following questions can be answered:
- Which class has the least attendance?
- Which teachers taugh the most students?
- What is the average number of attendance of a given course?
All the queries are based on the COUNT() with the GROUP BY queries. I think that the interesting metrics are the nested GROUP BY so you can first count and then apply other aggregate functions such as AVERAGE, MAX, MIX.
The other type of factless table is called Coverage table by Ralph. It is used to support negative analysis report. For example a Store that did not sell a product for a given period. To produce such report, you need to have a fact table to capture all the possible combinations. You can then figure out what is missing.
No tuple appears if there is no sale/purchase. You can have NULL values for surrogate keys.
Social media can have Fact-less Fact Table. Need to think about it !
Degenerated Key
Market Basket Analysis and associated promotions
Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer.
FACTS -> As granular as possible
Dimensions
AYFD Datamarts
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