Data Warehousing. Yeow Wei Choong Anne Laurent

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1 Data Warehousing Yeow Wei Choong Anne Laurent

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4 Databases Databases are developed on the IDEA that DATA is one of the cri>cal materials of the Informa>on Age Informa>on, which is created by data, becomes the bases for decision making

5 Decision Support Systems Created to facilitate the decision making process So much informa>on that it is difficult to extract it all from a tradi>onal database Need for a more comprehensive data storage facility Data Warehouse

6 Decision Support Systems Extract Informa>on from data to use as the basis for decision making Used at all levels of the Organiza>on Tailored to specific business areas Interac>ve Ad Hoc queries to retrieve and display informa>on Combines historical opera>on data with business ac>vi>es

7 4 Components of DSS 1. Data Store The DSS Database Business Data Business Model Data Internal and External Data 2. Data Extrac>on and Filtering Extract and validate data from the opera>onal database and the external data sources

8 4 Components of DSS 3. End- User Query Tools Create Queries that access either the Opera>onal or the DSS database 4. End- User Presenta>on Tools Organize and Present the Data

9 Data Store (Business Data) Data Extraction/ Filtering End-User Query Tools End-User Presentation Tools

10 Differences with DSS Opera>onal Stored in Normalized Rela>onal Database Support transac>ons that represent daily opera>ons (Not Query Friendly) 3 Main Differences Time Span Granularity Dimensionality

11 Time Span Opera>onal Real Time Current Transac>ons Short Time Frame Specific Data Facts DSS Historic Long Time Frame (Months/Quarters/Years) Pa_erns

12 Opera>onal Granularity Specific Transac>ons that occur at a given >me DSS Shown at different levels of aggrega>on Different Summary Levels Decompose (drill down) Summarize (roll up)

13 Dimensionality Most dis>nguishing characteris>c of DSS data Opera>onal Represents atomic transac>ons DSS Data is related in many ways Develop the larger picture Mul>- dimensional view of data

14 Data Cube

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18 DSS Database Requirements DSS Database Scheme Support Complex and Non- Normalized data Summarized and Aggregate data Mul>ple Rela>onships Queries must extract mul>- dimensional >me slices Redundant Data

19 Non- Normalized Data

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21 DSS Database Requirements Data Extrac>on and Filtering DSS databases are created mainly by extrac>ng data from opera>onal databases combined with data imported from external source Need for advanced data extrac>on & filtering tools Allow batch / scheduled data extrac>on Support different types of data sources Check for inconsistent data / data valida>on rules Support advanced data integra>on / data formaang conflicts

22 DSS Database Requirements End- User Analy>cal Interface Must support advanced data modeling and data presenta>on tools Data analysis tools Query genera>on Must Allow the User to Navigate through the DSS Size Requirements VERY Large Terabytes Advanced Hardware (Mul>ple processors, mul>ple disk arrays, etc.)

23 Very Large Databases

24 Very Large Databases

25 Data Warehouse DSS friendly data repository for the DSS is the DATA WAREHOUSE Defini>on: Integrated, Subject- Oriented, Time- Variant, Nonvola>le database that provides support for decision making

26 Data Warehouse

27 Integrated The data warehouse is a centralized, consolidated database that integrated data derived from the en>re organiza>on Mul>ple Sources Diverse Sources Diverse Formats

28 Subject- Oriented Data is arranged and op>mized to provide answer to ques>ons from diverse func>onal areas Data is organized and summarized by topic Sales / Marke>ng / Finance / Distribu>on / Etc.

29 Time- Variant The Data Warehouse represents the flow of data through >me Can contain projected data from sta>s>cal models Data is periodically uploaded then >me- dependent data is recomputed

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31 Nonvola>le Once data is entered it is NEVER removed Represents the company s en>re history Near term history is con>nually added to it Always growing Must support terabyte databases and mul>processors Read- Only database for data analysis and query processing

32 Data Marts Small Data Stores More manageable data sets Targeted to meet the needs of small groups within the organiza>on Small, Single- Subject data warehouse subset that provides decision support to a small group of people

33 Data Marts

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35 Data Marts

36 OLAP Online Analy>cal Processing Tools DSS tools that use mul>dimensional data analysis techniques Support for a DSS data store Data extrac>on and integra>on filter Specialized presenta>on interface

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38 12 Rules of a Data Warehouse 1. Data Warehouse and Opera>onal Environments are Separated 2. Data is integrated 3. Contains historical data over a long period of >me 4. Data is a snapshot data captured at a given point in >me 5. Data is subject- oriented

39 12 Rules of Data Warehouse 6. Mainly read- only with periodic batch updates 7. Development Life Cycle has a data driven approach versus the tradi>onal process- driven approach 8. Data contains several levels of detail 6. Current, Old, Lightly Summarized, Highly Summarized

40 12 Rules of Data Warehouse 9. Environment is characterized by Read- only transac>ons to very large data sets 10. System that traces data sources, transforma>ons, and storage 11. Metadata is a cri>cal component Source, transforma>on, integra>on, storage, rela>onships, history, etc 12. Contains a chargeback mechanism for resource usage that enforces op>mal use of data by end users

41 OLAP Need for More Intensive Decision Support 4 Main Characteris>cs Mul>dimensional data analysis Advanced Database Support Easy- to- use end- user interfaces Support Client/Server architecture

42 Mul>dimensional Data Analysis Techniques Advanced Data Presenta>on Func>ons 3- D graphics, Pivot Tables, Crosstabs, etc. Compa>ble with Spreadsheets & Sta>s>cal packages Advanced data aggrega>ons, consolida>on and classifica>on across >me dimensions Advanced computa>onal func>ons Advanced data modeling func>ons

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44 Advanced Database Support Advanced Data Access Features Access to many kinds of DBMS s, flat files, and internal and external data sources Access to aggregated data warehouse data Advanced data naviga>on (drill- downs and roll- ups) Ability to map end- user requests to the appropriate data source Support for Very Large Databases

45 Drill-down

46 Easy- to- Use End- User Interface Graphical User Interfaces (GUIs) Much more useful if access is kept simple

47 Client/Server Architecture Framework for the new systems to be designed, developed and implemented Divide the OLAP system into several components that define its architecture Same Computer Distributed among several computer

48 OLAP Architecture 3 Main Modules GUI Analy>cal Processing Logic Data- processing Logic

49 OLAP Client/Server Architecture

50 Rela>onal OLAP Rela>onal Online Analy>cal Processing OLAP func>onality using rela>onal database and familiar query tools to store and analyze mul>dimensional data Mul>dimensional data schema support Data access language & query performance for mul>dimensional data Support for Very Large Databases

51 Mul>dimensional Data Schema Support Decision Support Data tends to be Nonnormalized Duplicated Preaggregated Star Schema Special Design technique for mul>dimensional data representa>ons Op>mize data query opera>ons instead of data update opera>ons

52 Star Schemas Data Modeling Technique to map mul>dimensional decision support data into a rela>onal database Current Rela>onal modeling techniques do not serve the needs of advanced data requirements

53 4 Components Facts Dimensions A_ributes A_ribute Hierarchies Star Schema

54 4 Components Facts Dimensions A_ributes A_ribute Hierarchies Star Schema

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56 4 Components Facts Dimensions A_ributes A_ribute Hierarchies Star Schema

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58 Facts Numeric measurements (values) that represent a specific business aspect or ac>vity Stored in a fact table at the center of the star scheme Contains facts that are linked through their dimensions Can be computed or derived at run >me Updated periodically with data from opera>onal databases

59 Star Schema for Sales Dimension Tables Fact Table

60 Dimensions Qualifying characteris>cs that provide addi>onal perspec>ves to a given fact DSS data is almost always viewed in rela>on to other data Dimensions are normally stored in dimension tables

61 Star Schema for Sales Dimension Tables Members Fact Table

62 A_ributes Dimension Tables contain A_ributes A_ributes are used to search, filter, or classify facts Dimensions provide descrip>ve characteris>cs about the facts through their a_ributed Must define common business a_ributes that will be used to narrow a search, group informa>on, or describe dimensions. (ex.: Time / Loca>on / Product) No mathema>cal limit to the number of dimensions (3- D makes it easy to model)

63 A_ribute Hierarchies Provides a Top- Down data organiza>on Aggrega>on Drill- down / Roll- Up data analysis A_ributes from different dimensions can be grouped to form a hierarchy

64 Hierarchy: TIME TIME

65 Star Schema for Sales Dimension Tables Members Fact Table

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68 Star Schema Representa>on Fact and Dimensions are represented by physical tables in the data warehouse database Fact tables are related to each dimension table in a Many to One rela>onship (Primary/Foreign Key Rela>onships) Fact Table is related to many dimension tables The primary key of the fact table is a composite primary key from the dimension tables Each fact table is designed to answer a specific DSS ques>on

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70 Star Schema The fact table is always the largest table in the star schema Each dimension record is related to thousand of fact records Star Schema facilitated data retrieval func>ons DBMS first searches the Dimension Tables before the larger fact table Database Management System

71 Data Warehouse Implementa>on An Ac>ve Decision Support Framework Not a Sta>c Database Always a Work in Process Complete Infrastructure for Company- Wide decision support Hardware / Sonware / People / Procedures / Data Data Warehouse is a cri>cal component of the Modern DSS But not the Only cri>cal component

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78 How many dimensions in this representation?

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80 Inmon & Kimball - The Two Pioneers William Inmon Ralph Kimball

81 The Books

82 Exercise 1 What is the difference between a database and a data warehouse? Describe the followings: Data Marts Granularity Draw an example of a Star Schema

83 Exercise 2 Match the typical OLAP opera>ons: Roll up (drill- up) Drill down (roll up) Slice and dice Pivot (Rotate) Drill across Drill through

84 Exercise 3 1. What are the main dimensions of this fact table? 2. How are these dimensions linked to the fact table? 3. How can these operations permit fast queries, reports and data consolidations to be carried out? 4. From this star schema, construct an example of a query

85 END

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