1. Inventory is used to illustrate:
Chapter 3
Inventory is used to illustrate:
Data warehouse architecture
Bus
Matrix
Conformed dimensions
Conformed facts
Fact table types:
periodic snapshot
accumulating snapshot
transaction
Semi-additive facts
In chapter 3, the focus is primarily on the periodic snapshot fact table
January 2004
Ron McFadyen

2. {Note: in Chapter 2 we examined a “transaction” fact table}
Periodic Snapshot
At regular intervals some metric is calculated and recorded in a fact table – could be daily, weekly, monthly, … but its done at regular predictable times.
{Note: in Chapter 2 we examined a “transaction” fact table}
e.g. Inventory Periodic Snapshot
January 2004
Ron McFadyen

3. Inventory Periodic Snapshot
Date
Product
Store Inventory Snapshot
Store
January 2004
Ron McFadyen

4. Inventory Periodic Snapshot
Store Inventory Snapshot
Each day the quantity on hand is recorded for each product and store
We have a “snapshot” of inventory at the end of each day. Levels vary during the day, but we know what it is at the end-of-day.
Date key
Product key
Store key
Quantity on hand
January 2004
Ron McFadyen

5. Inventory Periodic Snapshot
Store Inventory Snapshot
An issue with this fact table is its size.
See text for example … 30 GB each year
Save space by:
Reducing the frequency
Reducing the number of days kept
Date key
Product key
Store key
Quantity on hand
January 2004
Ron McFadyen

6. Inventory Periodic Snapshot
Store Inventory Snapshot
Is the quantity on hand
Additive?
Semi-additive?
Non-additive?
Date key
Product key
Store key
Quantity on hand
Instead of adding quantities across time, if we average the metric, we may have a useful aggregation
January 2004
Ron McFadyen

7. Inventory Periodic Snapshot
Store Inventory Snapshot
Date key
Product key
Store key
Quantity on hand
E.g. Average inventory for a brand in a region for a given week?
Product  brand {3 products}
Store  region {4 stores}
Difficult to do in SQL …
January 2004
Ron McFadyen

8. Inventory Periodic Snapshot
Store Inventory Snapshot
Date key
Product key
Store key
Quantity on hand
Quantity sold
Value at cost
Value at latest selling price
Gross profit …
Other useful facts
January 2004
Ron McFadyen

9. Other fact tables for Inventory
Inventory Transaction Facts
Inventory Accumulating Snapshot Facts
January 2004
Ron McFadyen

10. Warehouse Inventory Transaction Facts
Product
Date
Warehouse Inventory Trans fact
Date key
Product key
Warehouse key
Vendor key
Inventory trans type key
Inventory trans dollar amount
Vendor
Warehouse
Trans type
January 2004
Ron McFadyen

11. Warehouse Inventory Transaction Facts
Transaction types:
Receive
Place in bin
Authorize for sale
Ship to customer
Remove from inventory …
January 2004
Ron McFadyen

12. Warehouse Inventory Accumulating Snapshot Facts
Provides an updated status of something as it moves through various states or milestones. Typically there are many dates – a date is related to each milestone.
A record is placed in the fact table, and then updated as milestones are reached.
One can measure the velocity of something moving through the system
January 2004
Ron McFadyen

13. Warehouse Inventory Accumulating Snapshot Facts
Product
Date received
Warehouse Inventory Accumulating fact
Date received key
Date inspected key
Date placed in inventory key
Date authorized to sell key
Date picked key
Date boxed key
Date shipped key
Date of last return key
Product key
Warehouse key
Vendor key
…facts…
Date inspected
Vendor
How many Date tables are there?
Warehouse
January 2004
Ron McFadyen

14. Warehouse Inventory Accumulating Snapshot Facts
Product
Date received
Date inspected
Warehouse Inventory Accumulating fact
Vendor
The fact table must be updated from time to time
(again in Chapter 5)
Warehouse
January 2004
Ron McFadyen

15. The chapter illustrates several potential data marts.
Conformed dimensions
The chapter illustrates several potential data marts.
To make best use of these, it is recommended that dimensions be reused.
This allows us to query multiple stars and combine results.
Multipass SQL : each star is queried separately and the results combined/merged.
Querying multiple stars is referred to as drill-across
e.g. What are the sales amounts for products that represent the top 80% of inventory value?
January 2004
Ron McFadyen

16. Conformed dimensions Dim 1 Fact table 1 Dim 4 Fact table 2 Dim 2
January 2004
Ron McFadyen

17. Bus Architecture
The Bus Architecture relates dimensions to data marts, promoting re-usability.
As new data marts are added they plug into the architecture, reusing existing dimensions; adding others
e.g. Time and Product dimensions are reused in the Orders and Production data marts
Orders
Production
Dimensions
Time
Sales Rep
Customer
Promotion
Product
Plant
Distr. Center
January 2004
Ron McFadyen

18. Data warehouses are built one data mart at a time - iteratively.
Bus Architecture
Data warehouses are built one data mart at a time - iteratively.
This architectural framework relates each of these marts to one another. The objective is to build dimensions once, promote their reuse (the same dimension appears in many star schemas) and be able to deliver successive data marts faster.
This architecture relies on building conformed dimensions and conformed facts.
Conformed dimensions make it possible to perform analyses across data marts.
January 2004
Ron McFadyen

19. Likely between 10 and 30 data marts
Bus Architecture
One data mart at a time. Each project may be 3 to 6 months in duration.
Data warehouse construction begins with a planning phase to identify potential data marts and dimensions.
Create a matrix
Likely between 10 and 30 data marts
January 2004
Ron McFadyen

20. Data Warehouse Bus Matrix
A useful planning tool
Business processes are rows; these become data marts
Dimensions are columns
Illustrates re-use of dimensions
Illustrates complexities in data mart construction
Can be used to guide which data mart to build first/next
Recommendation for first data mart is one that is easy to build – limit the number and complexities in source systems
January 2004
Ron McFadyen

21. Matrix assumes that dimensions are re-usable
Conformed dimensions
Matrix assumes that dimensions are re-usable
Different data marts either use
Same table
A synchronous copy
A subset dimension
Includes horizontal/vertical partition
Includes roll-up dimensions (occur with aggregation – see figure 3.9)
January 2004
Ron McFadyen

22. This is a very disciplined approach to naming attributes.
Conformed facts
If facts are given the same name in two fact tables, they are conformed if they have the same definition and formula.
If two facts are different in some way, then they must be given different names.
This is a very disciplined approach to naming attributes.
January 2004
Ron McFadyen

23. Summary of topics Architecture Facts Additive Semi-additive
Bus
Matrix
Conformed dimensions
Conformed facts
Facts
Additive
Semi-additive
Non-additive
Types of fact tables:
periodic snapshot
accumulating snapshot
transaction
January 2004
Ron McFadyen