Data Warehouse and the Star Schema CSCI 242 ©Copyright 2014, David C. Roberts, all rights reserved

2 Agenda Definition Why data warehouse Data warehouse in the enterprise Data warehouse design Relevance of normalization Star schema Processing the star schema

3 Definition Data warehouse: A repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated The point is that it’s not used for transaction processing; that is, it’s read-only. And the data can come from heterogeneous sources and it can all be queried in one database.

4 Why Data Warehouse A read lock on a table will prevent any updating of a table A long-running analytic operation on all rows of a table will prevent any updates Analysis (a.k.a. decision support) can seriously interfere with updates Using a duplicate table for analysis, recopied once a day, allows unlimited analysis and doesn’t interfere with OLTP.

5 Data Warehouse vs. OLTP OLTPDW PurposeAutomate day-to-day operations Analysis StructureRDBMSRMBMS Data ModelNormalizedDimensional AccessSQLSQL and business analysis programs DataData that runs the businessCurrent and historical information Condition of dataChanging, incompleteHistorical, complete, descriptive

6 How It Fits into the Enterprise OLTP3 Data Mart Data Warehouse Data Mart Data Mart Data Mart Application A Application B Application C User Extract, Transform And Load OLTP2 OLTP1 Integration

7 Data Warehouse Database Design A conventional database design for data warehouse would lead to joins on large amounts of data that would run slowly The star schema allows for fast processing of very large quantities of data in the data warehouse

8 A Sample OLTP Schema orders products order items customers

9 Transformed to a Star Schema products customers sales channels times fact table dimension table dimension table dimension table dimension table

10 Star Schema Fact Table Customer Item Supplier Time Location

11 Fact Table The fact table contains the actual business process measurements or metrics called facts, usually numbers. Other aspects of the business process are represented by foreign keys to “dimension” tables. These foreign keys are usually generated keys, in order to save fact table space If you are building a DW of monthly sales in dollars, your fact table will contain monthly sales, one row per month. If you are building a DW of retail sales, each row of the fact table might have one row for each item sold.

12 Fact Table Design A fact table may contain one or more facts. Usually you create one fact table per business process or event. For example if you want to analyze the sales numbers and also advertising spending, they are two separate business processes. So you will create two separate fact tables, one for sales data and one for advertising cost data. On the other hand if you want to track the sales tax in addition to the sales number, you simply create one more fact column in the Sales fact table called Tax.

13 Dimension Table Dimension tables are used to provide context for the measurements that are presented in the fact table. Think of the context of a measurement as the who, what, where, when, how of a measurement. In an example business process Sales, the characteristics of the 'monthly sales number' measurement can be a Location (Where), Time (When), Product Sold (What). Dimension attributes may contain hierarchical relationships, such as grouping time into day, week, month, year, etc.

14 Time Dimension Schema Field NameType Dim_IdINTEGER (4) MonthSMALL INTEGER (2) Month_NameVARCHAR (3) QuarterSMALL INTEGER (4) Quarter_NameVARCHAR (2) YearSMALL INTEGER (2)

15 Time Dimension Data TM _Dim_IdTM _MonthTM_Month_NameTM _Quarter TM_Quarter_N ame TM_Year Jan1Q Feb1Q Mar1Q Apr2Q May2Q22003

16 Location Dimension Schema Field NameType Dim_IdINTEGER (4) Loc_CodeVARCHAR (4) NameVARCHAR (50) State_NameVARCHAR (20) Country_NameVARCHAR (20)

17 Location Dimension Data Dim_IdLoc_CodeNameState_NameCountry_Name 1001 IL01Chicago LoopIllinoisUSA 1002 IL02Arlington HtsIllinoisUSA 1003NY01BrooklynNew YorkUSA 1004TO01TorontoOntarioCanada 1005MX01Mexico CityDistrito FederalMexico

18 Product Data Schema Field NameType Dim_IdINTEGER (4) SKUVARCHAR (10) NameVARCHAR (30) CategoryVARCHAR (30)

19 Product Data Dim_IdSKUNameCategory 1001DOVE6KDove Soap 6PkSanitary 1002MLK66F#Skim Milk 1 GalDairy 1003SMKSAL55Smoked Salmon 6ozMeat

Categories in Dimension Tables Categories may or may not be hierarchical; or can be both Categories provide canned values that can be given to users for queries 20

21 Granularity (Grain) of the Fact Table The level of detail of the fact table is known as the grain of the fact table. In this example the grain of the fact table is monthly sales number per location per product.

Note about Granularity There may be multiple star schemas at different levels of granularity, especially for very large data warehouses The first could be the finest—say, each transaction such as a sale The next could be an aggregation, like the previous example There could be more levels of aggregation 22

23 Design Approach 1. Identify the business process. In this step you will determine what is your business process that your data warehouse represents. This process will be the source of your metrics or measurements. 2. Identify the Grain You will determine what does one row of fact table mean. In the previous example you have decided that your grain is 'monthly sales per location per product'. It might be daily sales or even each sale could be one row. 3. Identify the Dimensions Your dimensions should be descriptive (SQL VARCHAR or CHARACTER) as much as possible and conform to your grain. 4. Finally Identify the facts In this step you will identify what are your measurements (or metrics or facts). The facts should be numeric and should confirm to the grain defined in step 2.

24 Monthly Sales Fact Table Schema Field NameType TM_Dim_IdINTEGER (4) PR_ Dim_IdINTEGER (4) LOC_ Dim_IdINTEGER (4) SalesINTEGER (4)

25 Monthly Sales Fact Table Data TM_Dim_IdPR_ Dim_IdLOC_ Dim_IdSales

26 Data Mart A data mart is a collection of subject areas organized for decision support based on the needs of a given department. Examples: finance has their data mart, marketing has theirs, sales has theirs and so on. Each department generally runs its own data mart. Ownership of the data mart allows each department to bypass the control that might coordinate the data found in the different departments. Each department's data mart is peculiar to and specific to its own needs. Typically, the database design for a data mart is built around a star-join structure designed for that department. The data mart contains only a modicum of historical information and is granular only to the point that it suits the needs of the department. The data mart may also include data from outside the organization, such as purchased normative salary data that might be purchased by an HR department.

27 About the Data Mart The structure of the data in the data mart may or may not be compatible with the structure of data in the data warehouse. The amount of historical data found in the data mart is different from the history of the data found in the warehouse. Data warehouses contain robust amounts of history, while data marts usually contain modest amounts of history. The subject areas found in the data mart are only faintly related to the subject areas found in the data warehouse. The relationships found in the data mart may not be those relationships that are found in the data warehouse. The types of queries satisfied in the data mart are quite different from those queries found in the data warehouse.

Walmart’s Data Warehouse Half a petabyte in capacity (.5 x bytes) World’s largest DW Tracks 100 million customers buying billions of products every week Every sale from every store is transmitted to Bentonville every night Walmart has more than 18,000 retail stores, employs 2.2 million, serves 245 million customers every week 28

Typical Questions How much orange juice did we sell last year, last month, last week in store X? What internal factors (position in store, advertising campaigns...) influence orange juice sales? How much orange juice are we going to sell next week, next month, next year? 29

30