Data Warehouse and the Star Schema CSCI 242

Data Warehouse and the Star Schema CSCI 242 ©Copyright 2019, David C. Roberts, all rights reserved

Finally we are talking about something not invented by IBM! Inventor is unknown. Popularized by Ralph Kimball and his company, Red Brick Warehouse. 2

History l l l 3 First product introduced by Red Brick Warehouse, a standalone system for data warehouse Algorithm was figured out by Oracle and Sybase. Oracle built into DBMS, Sybase made separate software product. IBM bought Red Brick

Agenda l l l l 4 l l Definition Why data warehouse Product History Processing Star queries Data warehouse in the enterprise Data warehouse design Relevance of normalization Star schema Processing the star schema

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. 5

Why Data Warehouse l l l 6 Analytical use of a database involves lots of reading, and some table scanning Reading and scanning interferes with update but not with other reading and scanning It makes sense to have a separate copy of data for analytics, recopied once a day

Data Warehouse vs. OLTP 7 DW Purpose Automate day-to-day operations Analysis Structure RDBMS RMBMS Data Model Normalized Dimensional Access SQL and business analysis programs Data that runs the business Current and historical information Condition of data Changing, incomplete Historical, complete, descriptive

Red Brick l l l 8 Invented data warehouse; they sold a hardware product with a star schema database You loaded the Red Brick Warehouse and then queried it for OLTP It featured new optimizations for star schemas, was very fast

Enter Sybase l l 9 Sybase learned the optimization and developed their own product. The Sybase product was a stand-alone software data warehouse product It couldn’t do general-purpose database work, was just a data warehouse They appear to have copied the Red Brick idea, without selling hardware

Enter Oracle l l l 10 Oracle, later, also copied the same optimization They added a bitmap index to their database product, and added the star schema optimization Now their product could do data warehouse as well as database

Status Today l l l 11 Oracle dominates the field today IBM eventually bought Red Brick so still offers some sort of Red Brick product Sybase offers their OLTP product, now as an offering of SAP

So what is this algorithm that is so copied? PROCESSING STAR QUERIES 12

Star Schema l l l 13 Data warehouse relies on the star schema The data is not normalized DW is loaded from a normalized database There is a fact table surrounded by multiple dimension tables Fact table has all measures for the subject area, with foreign keys for dimensions for each measure

A Sample OLTP Schema customers order items 14 products

Transformed to a Star Schema Time products Day of Week times Time dimension table Hour Product Customer sales Channel dimension table fact table 15 customers channels dimension table Month Year Season

Star Schema Time Location Item Fact Table Supplier Customer 16

Processing Star Queries l l l 17 Build a bitmap index on each foreign key column of the fact table Index is a 2 -dimensional array, one column for each row being indexed, one row per value of that column Bitmap indexes are typically much smaller than b-tree indexes, that can be larger than the data itself

Bitmap Index Example 18

Query Processing l l The typical query is a join of foreign keys of dimension tables to the fact table This is processed in two phases: 1. 2. 19 From the fact table, retrieve all rows that are part of the result, using bitmap indexes Join the result of the step above to the dimension tables

Example Query Find sales and profits from the grocery departments of stores in the West and Southwest districts over the last three quarters 20

Example Query SELECT store. sales_district, time. fiscal_period, SUM(sales. dollar_sales) revenue, SUM(dollar_sales) - SUM(dollar_cost) income FROM sales, store, time, product WHERE sales. store_key = store_key AND sales. time_key = time_key AND sales. product_key = product_key AND time. fiscal_period IN ('3 Q 95', '4 Q 95', '1 Q 96') and product. department = 'Grocery' AND store. sales_district IN ('San Francisco', 'Los Angeles') GROUP BY store. sales_district, time. fiscal_period; 21

Phase 1 Finding the rows in the SALES table (using bitmap indexes): SELECT. . . FROM sales WHERE store_key IN (SELECT store_key FROM store WHERE sales_district IN ('WEST', 'SOUTHWEST')) AND time_key IN (SELECT time_key FROM time WHERE quarter IN ('3 Q 96', '4 Q 96', '1 Q 97')) AND product_key IN (SELECT product_key FROM product WHERE department = 'GROCERY'); 22

Phase 2 Now the fact table is joined to dimension tables. For dimension tables of small cardinality, a fulltable scan may be used. For large cardinality, a hash join could be used. 23

The Star Transformation l Use bitmap indexes to retrieve all relevant rows from the fact table, based on foreign key values – l Join this result set to the dimension tables – – 24 This happens very fast If there are many values, a hash join may be used If there are fewer values, a b-tree driven join may be used

How DW Fits into the Enterprise User Application A User OLTP 1 Integration User Data Warehouse Application B OLTP 2 User 25 Extract, Transform And Load Integration Application C Data Mart OLTP 3 User Data Mart

Data Warehouse Database Design l l l 26 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 It also allows for very compact representation of events that occur many times

A Sample OLTP Schema customers order items 27 products

Transformed to a Star Schema products times dimension table sales fact table 28 customers channels dimension table

Star Schema Time Location Item Fact Table Supplier Customer 29

Fact Table l l l 30 The fact table contains the actual business process measurements or metrics for a specific event, called facts, usually numbers. A fact table represents facts by foreign keys from other tables, called “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.

Fact Table Design A fact table may contain one or more facts. Usually you create one fact table per business 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. 31

Dimension Table l l l 32 Dimension tables have a small number of rows (compared to fact tables) but a large number of columns For the lowest level of granularity of a fact in the fact table, a dimension table will have one row that gives all the categories for each value The dimension table is often all key, so a generated key is used so that the fact table reference to the dimension table can be small

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Time Dimension Schema 34 Column Name Type Dim_Id INTEGER (4) Month SMALL INTEGER (2) Month_Name VARCHAR (3) Quarter SMALL INTEGER (4) Quarter_Name VARCHAR (2) Year SMALL INTEGER (2)

Time Dimension Data 35 TM _Dim_Id TM _Month TM_Month_Name TM _Quarter TM_Quarter_N ame TM_Year 1001 1 Jan 1 Q 1 2003 1002 2 Feb 1 Q 1 2003 1003 3 Mar 1 Q 1 2003 1004 4 Apr 2 Q 2 2003 1005 5 May 2 Q 2 2003

Location Dimension Schema 36 Column Name Type Dim_Id INTEGER (4) Loc_Code VARCHAR (4) Name VARCHAR (50) State_Name VARCHAR (20) Country_Name VARCHAR (20)

Location Dimension Data Dim_Id 37 Loc_Code Name State_Name Country_Name 1001 IL 01 Chicago Loop Illinois USA 1002 IL 02 Arlington Hts Illinois USA 1003 NY 01 Brooklyn New York USA 1004 TO 01 Toronto Ontario Canada 1005 MX 01 Mexico City Distrito Federal Mexico

Product Data Schema 38 Column Name Type Dim_Id INTEGER (4) SKU VARCHAR (10) Name VARCHAR (30) Category VARCHAR (30)

Product Data 39 Dim_Id SKU Name Category 1001 DOVE 6 K Dove Soap 6 Pk Sanitary 1002 MLK 66 F# Skim Milk 1 Gal Dairy 1003 SMKSAL 55 Smoked Salmon 6 oz Meat

Categories in Dimension Tables l l 40 Categories may or may not be hierarchical; or can be both Categories provide canned values that can be given to users for queries

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. 41

Note about Granularity l l 42 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

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. 43 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.

Monthly Sales Fact Table Schema 44 Field Name Type TM_Dim_Id INTEGER (4) PR_ Dim_Id INTEGER (4) LOC_ Dim_Id INTEGER (4) Sales INTEGER (4)

Monthly Sales Fact Table Data 45 TM_Dim_Id PR_ Dim_Id LOC_ Dim_Id Sales 1001 1003 435677 1002 1001 451121 1003 1001 1003 98765 1001 1004 1001 65432

Data Mart l l l 46 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.

About the Data Mart l l l 47 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 l l l 48 Half a petabyte in capacity (. 5 x 1015 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

Typical Questions l l l 49 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?

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