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 91.4904 Ron McFadyen

{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 91.4904 Ron McFadyen

Inventory Periodic Snapshot Date Product Store Inventory Snapshot Store January 2004 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

Other fact tables for Inventory Inventory Transaction Facts Inventory Accumulating Snapshot Facts January 2004 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

Warehouse Inventory Transaction Facts Transaction types: Receive Place in bin Authorize for sale Ship to customer Remove from inventory … January 2004 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

Conformed dimensions Dim 1 Fact table 1 Dim 4 Fact table 2 Dim 2 January 2004 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen

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 91.4904 Ron McFadyen