© 2007 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 8 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden

Chapter 11 © 2007 by Prentice Hall 2 Objectives Definition of terms Definition of terms Reasons for information gap between information needs and availability Reasons for information gap between information needs and availability Reasons for need of data warehousing Reasons for need of data warehousing Describe three levels of data warehouse architectures Describe three levels of data warehouse architectures List four steps of data reconciliation List four steps of data reconciliation Describe two components of star schema Describe two components of star schema Estimate fact table size Estimate fact table size Design a data mart Design a data mart

Chapter 11 © 2007 by Prentice Hall 3Definition Data Warehouse: Data Warehouse: A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management decision-making processes A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management decision-making processes Subject-oriented: e.g. customers, patients, students, products Subject-oriented: e.g. customers, patients, students, products Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources Time-variant: Can study trends and changes Time-variant: Can study trends and changes Nonupdatable: Read-only, periodically refreshed Nonupdatable: Read-only, periodically refreshed Data Mart: Data Mart: A data warehouse that is limited in scope A data warehouse that is limited in scope

Chapter 11 © 2007 by Prentice Hall 4 Need for Data Warehousing Integrated, company-wide view of high-quality information (from disparate databases) Integrated, company-wide view of high-quality information (from disparate databases) Separation of operational and informational systems and data (for improved performance) Separation of operational and informational systems and data (for improved performance)

Chapter 11 © 2007 by Prentice Hall 5 Source: adapted from Strange (1997).

Chapter 11 © 2007 by Prentice Hall 6 Data Warehouse Architectures Generic Two-Level Architecture Generic Two-Level Architecture Independent Data Mart Independent Data Mart Dependent Data Mart and Operational Data Store Dependent Data Mart and Operational Data Store Logical Data Mart and Real-Time Data Warehouse Logical Data Mart and Real-Time Data Warehouse Three-Layer architecture Three-Layer architecture ETL All involve some form of extraction, transformation and loading (ETL)

Chapter 11 © 2007 by Prentice Hall 7 Figure 11-2: Generic two-level data warehousing architecture E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

Chapter 11 © 2007 by Prentice Hall 8 Figure 11-3 Independent data mart data warehousing architecture Data marts: Mini-warehouses, limited in scope E T L Separate ETL for each independent data mart Data access complexity due to multiple data marts

Chapter 11 © 2007 by Prentice Hall 9 Figure 11-4 Dependent data mart with operational data store: a three-level architecture E T L Single ETL for enterprise data warehouse(EDW) Simpler data access ODS ODS provides option for obtaining current data Dependent data marts loaded from EDW

Chapter 11 © 2007 by Prentice Hall 10 E T L Near real-time ETL for Data Warehouse ODS data warehouse ODS and data warehouse are one and the same Data marts are NOT separate databases, but logical views of the data warehouse  Easier to create new data marts Figure 11-5 Logical data mart and real time warehouse architecture

Chapter 11 © 2007 by Prentice Hall 11 Figure 11-6 Three-layer data architecture for a data warehouse

Chapter 11 © 2007 by Prentice Hall 12 Data Characteristics Status vs. Event Data Status Event = a database action (create/update/delete) that results from a transaction Figure 11-7 Example of DBMS log entry

Chapter 11 © 2007 by Prentice Hall 13 Data Characteristics Transient vs. Periodic Data With transient data, changes to existing records are written over previous records, thus destroying the previous data content Figure 11-8 Transient operational data

Chapter 11 © 2007 by Prentice Hall 14 Periodic data are never physically altered or deleted once they have been added to the store Data Characteristics Transient vs. Periodic Data Figure 11-9: Periodic warehouse data

Chapter 11 © 2007 by Prentice Hall 15 Other Data Warehouse Changes New descriptive attributes New descriptive attributes New business activity attributes New business activity attributes New classes of descriptive attributes New classes of descriptive attributes Descriptive attributes become more refined Descriptive attributes become more refined Descriptive data are related to one another Descriptive data are related to one another New source of data New source of data

Chapter 11 © 2007 by Prentice Hall 16 The Reconciled Data Layer Typical operational data is: Typical operational data is: Transient–not historical Transient–not historical Not normalized (perhaps due to denormalization for performance) Not normalized (perhaps due to denormalization for performance) Restricted in scope–not comprehensive Restricted in scope–not comprehensive Sometimes poor quality–inconsistencies and errors Sometimes poor quality–inconsistencies and errors After ETL, data should be: After ETL, data should be: Detailed–not summarized yet Detailed–not summarized yet Historical–periodic Historical–periodic Normalized–3 rd normal form or higher Normalized–3 rd normal form or higher Comprehensive–enterprise-wide perspective Comprehensive–enterprise-wide perspective Timely–data should be current enough to assist decision- making Timely–data should be current enough to assist decision- making Quality controlled–accurate with full integrity Quality controlled–accurate with full integrity

Chapter 11 © 2007 by Prentice Hall 17 The ETL Process Capture/Extract Capture/Extract Scrub or data cleansing Scrub or data cleansing Transform Transform Load and Index Load and Index ETL = Extract, transform, and load

Chapter 11 © 2007 by Prentice Hall 18 Static extract Static extract = capturing a snapshot of the source data at a point in time Incremental extract Incremental extract = capturing changes that have occurred since the last static extract Capture/Extract…obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse Figure 11-10: Steps in data reconciliation

Chapter 11 © 2007 by Prentice Hall 19 Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data quality Fixing errors: Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies Also: Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data Figure 11-10: Steps in data reconciliation (cont.)

Chapter 11 © 2007 by Prentice Hall 20 Transform = convert data from format of operational system to format of data warehouse Record-level: Selection–data partitioning Joining–data combining Aggregation–data summarization Field-level: single-field–from one field to one field multi-field–from many fields to one, or one field to many Figure 11-10: Steps in data reconciliation (cont.)

Chapter 11 © 2007 by Prentice Hall 21 Load/Index= place transformed data into the warehouse and create indexes Refresh mode: Refresh mode: bulk rewriting of target data at periodic intervals Update mode: Update mode: only changes in source data are written to data warehouse Figure 11-10: Steps in data reconciliation (cont.)

Chapter 11 © 2007 by Prentice Hall 22 Figure 11-11: Single-field transformation In general–some transformation function translates data from old form to new form Algorithmic transformation uses a formula or logical expression Table lookup–another approach, uses a separate table keyed by source record code

Chapter 11 © 2007 by Prentice Hall 23 Figure 11-12: Multifield transformation M:1–from many source fields to one target field 1:M–from one source field to many target fields

Chapter 11 © 2007 by Prentice Hall 24 Derived Data Objectives Objectives Ease of use for decision support applications Ease of use for decision support applications Fast response to predefined user queries Fast response to predefined user queries Customized data for particular target audiences Customized data for particular target audiences Ad-hoc query support Ad-hoc query support Data mining capabilities Data mining capabilities Characteristics Characteristics Detailed (mostly periodic) data Detailed (mostly periodic) data Aggregate (for summary) Aggregate (for summary) Distributed (to departmental servers) Distributed (to departmental servers) star schema Most common data model = star schema (also called “dimensional model”)

Chapter 11 © 2007 by Prentice Hall 25 star schema Figure Components of a star schema Fact tables contain factual or quantitative data Dimension tables contain descriptions about the subjects of the business 1:N relationship between dimension tables and fact tables Excellent for ad-hoc queries, but bad for online transaction processing Dimension tables are denormalized to maximize performance

Chapter 11 © 2007 by Prentice Hall 26 Figure Star schema example Fact table provides statistics for sales broken down by product, period and store dimensions

Chapter 11 © 2007 by Prentice Hall 27 Figure Star schema with sample data

Chapter 11 © 2007 by Prentice Hall 28 Issues Regarding Star Schema Dimension table keys must be surrogate (non- intelligent and non-business related), because: Dimension table keys must be surrogate (non- intelligent and non-business related), because: Keys may change over time Keys may change over time Length/format consistency Length/format consistency Granularity of Fact Table–what level of detail do you want? Granularity of Fact Table–what level of detail do you want? Transactional grain–finest level Transactional grain–finest level Aggregated grain–more summarized Aggregated grain–more summarized Finer grains  better market basket analysis capability Finer grains  better market basket analysis capability Finer grain  more dimension tables, more rows in fact table Finer grain  more dimension tables, more rows in fact table Duration of the database–how much history should be kept? Duration of the database–how much history should be kept? Natural duration–13 months or 5 quarters Natural duration–13 months or 5 quarters Financial institutions may need longer duration Financial institutions may need longer duration Older data is more difficult to source and cleanse Older data is more difficult to source and cleanse

Chapter 11 © 2007 by Prentice Hall 29 Figure 11-16: Modeling dates Fact tables contain time-period data  Date dimensions are important

Chapter 11 © 2007 by Prentice Hall 30 The User Interface Metadata (data catalog) Identify subjects of the data mart Identify subjects of the data mart Identify dimensions and facts Identify dimensions and facts Indicate how data is derived from enterprise data warehouses, including derivation rules Indicate how data is derived from enterprise data warehouses, including derivation rules Indicate how data is derived from operational data store, including derivation rules Indicate how data is derived from operational data store, including derivation rules Identify available reports and predefined queries Identify available reports and predefined queries Identify data analysis techniques (e.g. drill-down) Identify data analysis techniques (e.g. drill-down) Identify responsible people Identify responsible people

Chapter 11 © 2007 by Prentice Hall 31 On-Line Analytical Processing (OLAP) Tools The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques Relational OLAP (ROLAP) Relational OLAP (ROLAP) Traditional relational representation Traditional relational representation Multidimensional OLAP (MOLAP) Multidimensional OLAP (MOLAP) Cube structure Cube structure OLAP Operations OLAP Operations Cube slicing–come up with 2-D view of data Cube slicing–come up with 2-D view of data Drill-down–going from summary to more detailed views Drill-down–going from summary to more detailed views

Chapter 11 © 2007 by Prentice Hall 32 Figure Slicing a data cube

Chapter 11 © 2007 by Prentice Hall 33 Figure Example of drill-down Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells

Chapter 11 © 2007 by Prentice Hall 34 Data Mining and Visualization Knowledge discovery using a blend of statistical, AI, and computer graphics techniques Knowledge discovery using a blend of statistical, AI, and computer graphics techniques Goals: Goals: Explain observed events or conditions Explain observed events or conditions Confirm hypotheses Confirm hypotheses Explore data for new or unexpected relationships Explore data for new or unexpected relationships Techniques Techniques Statistical regression Statistical regression Decision tree induction Decision tree induction Clustering and signal processing Clustering and signal processing Affinity Affinity Sequence association Sequence association Case-based reasoning Case-based reasoning Rule discovery Rule discovery Neural nets Neural nets Fractals Fractals Data visualization–representing data in graphical/multimedia formats for analysis Data visualization–representing data in graphical/multimedia formats for analysis