Lecture 08B - Logical-DWH-Model-Pending.pptx
- 1. Logical Data Modeling Data Warehousing
- 2. Components of Dimensional Model
- 3. Star
- 4. Fact Table
- 6. What's Wrong w/This Fact Table of Basketball Player game stats?
- 9. Fact Table
- 10. Dimension Table –Should always contain a business key, or legacy PK from source system. –Always have a Surrogate Primary Key.+ –Dimension attributes can sometime be discrete numbers values that behave like text like Phone number –During meetings with Business, Dimension attribute are often heard following the word “By” (by year, by product, by region) –Dimension attribute should be verbose, descriptive, complete, discreetly valued and quality assured.
- 11. Dimension Table • Dimension rows are uniquely identified by a single key field. • Always have a Surrogate Primary Key. • Should always contain a business key, or legacy PK from source system. • A 4 byte integer makes a great surrogate key as it can represent more than 2 billion +ve integers
- 14. Conformed dimensions - example
- 15. Date and Time dimensions ›This is the most common conformed dimensions. ›Acceptable to use PK in the form YYYYMMDD ›In Case you need time of day, use a separate dimension. ›Time of day should only be used if there are meaningful textual descriptions of time –Ex.Lunch, Dinner, 1stshift, 2ndShift, Etc… ›Elapsed times intervals are facts, not attributes. –Ex. Minutes between when order was received and shipped
- 16. Date dimension
- 17. Degenerate dimension ›These are the dimensions which we store in the fact table ›These occur in transaction fact tables as actual transaction number without joining to anything e.g. Order Number, Invoice Number, Ticket Number ›It can also be used as a link back to operational systems ›Often used as part of PK of Fact table
- 18. Slowly changing dimensions Dimensional data changes infrequently but when it does you need a strategy for addressing the change. –Ex:What happens when a customer has a new address, or an Employee has a name change? 3 Popular strategies -Type 1: Overwrite the existing attribute -Type 2: Add a new Dimension row -Type 3: Add a new Dimension attribute –
- 19. Type 1 - Overwrite the existing attribute
- 20. Type 2: Add New Dimension Row ( Insert-Update) ›Most popular strategy, as it preserves history ›Natural key is repeated. ›Old and new values are stored along with effective dates and indicator of which row is “current”
- 21. Type 3: Add A New Dimension Attribute • Infrequently used, preserves partial history • Useful for “Soft” changes where users might want to choose between the old and new attribute, or need to access both values for a time. • The new value is written to the existing column, the old value is stored in a new column. • This way queries do not have to be re-written to access the new attribute.
- 22. Type 3 Example
- 24. Junk dimensions
- 25. Fact-less Facts • Business processes that do not generate quantifiable measurements • Ex: Student attendance, College admissions • Can be easily converted into traditional fact tables by adding an attribute Count, which is always equal to 1. • Consider adding facts for when the event did not happen • Helps to perform aggregations • Ex: Attendance % present or absent versus class size.
- 26. Fact-less Fact Table Example
- 27. Consolidated fact tables Fact tables populated from different sources may consolidated into single fact table –Level of granularity must be the same –Measurements are listed side-by-side –Ex. by combining forecast and actual sales amounts, a forecast/actual sales variance amount can be easily calculated and stored
- 28. Designing the Dimension model • Identify the required team roles and participants. • Review the business requirements document • Review the source data • Use the modelling tool • Establish Naming Conventions • Obtain facilities and supplies
- 29. Four Step Modelling Process
- 30. Four Step Modelling Process
- 32. High Level Domain Model
- 33. Design the Dimension Model
- 34. Step by Step Approach to Dimensional Modeling
- 35. Step by Step Approach to Dimensional Modeling ›Choose the business process –number of burgers and fries sold from a specific McDonalds outlet per day. –hence the Business Process is “Fast food Sales” ›Declare the Grain –granularity refers to the lowest (or most granular) level of information stored in any table –how many burgers and fries are getting sold from a specific McDonalds outlet per day –Hence the Grain is “food getting sold per store per day”
- 36. Step by Step Approach to Dimensional Modeling • ›Identify the dimensions • –Dimensions are the object or context. dimensions are the 'things' about which something is being spoken • –we are speaking about some "food", some specific McDonalds "store" and some specific “"day“ • –Hence we have 3 dimensions -"food" (e.g. burgers and fries), "store" and "day“ • –we need to know what are the attributes of each dimension that we need to store in our table • –let's take the dimension "food“, different attributes of food -e.g. names of the food, Type, price of the food, total calories in the food, contents, expiry date and so on
- 37. Step by Step Approach to Dimensional Modeling
- 38. Step by Step Approach to Dimensional Modeling
- 39. Step by Step Approach to Dimensional Modeling ›Identify the facts –Measures are the quantifiable subjects and these are often numeric in nature –the number of burgers/fries sold is a measure –a separate Fact table is created for storing measures. –since in our case the granularity is food getting sold per store per day, we will need to add key columns from food/store and Day dimensions to the “Sales Fact” Table
- 40. Step by Step Approach to Dimensional Modeling
- 42. Business Intelligence or Semantic Layer
- 43. • A business representation of corporate data that helps end users access data using common business terms. • The aim is to insulate users from the technical details of the data warehouse and allow them to create queries in terms that are familiar and meaningful. • One of the key components of the business intelligence (BI) architecture.
- 44. Types of BI Applications › Direct Access query and reporting tools › Data Mining › Standard Reports › Analytic Applications › Dashboards and Scorecards › Operational BI
- 45. Dashboards