© 2007 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 8 th Edition...

© 2007 by Prentice Hall© 2007 by Prentice Hall 11

Chapter 11:Chapter 11: Data Warehousing Data Warehousing

Modern Database Modern Database ManagementManagement

88thth Edition EditionJeffrey A. Hoffer, Mary B. Prescott, Jeffrey A. Hoffer, Mary B. Prescott,

Fred R. McFaddenFred R. McFadden

22Chapter 11 © 2007 by Prentice Hall© 2007 by Prentice Hall

ObjectivesObjectives Definition of termsDefinition of terms Reasons for information gap between Reasons for information gap between

information needs and availabilityinformation needs and availability Reasons for need of data warehousingReasons for need of data warehousing Describe three levels of data warehouse Describe three levels of data warehouse

architecturesarchitectures List four steps of data reconciliationList four steps of data reconciliation Describe two components of star schemaDescribe two components of star schema Estimate fact table sizeEstimate fact table size Design a data martDesign a data mart


DefinitionDefinition Data WarehouseData Warehouse: :

A subject-oriented, integrated, time-variant, non-A subject-oriented, integrated, time-variant, non-updatable collection of data used in support of updatable collection of data used in support of management decision-making processesmanagement decision-making processes

Subject-oriented:Subject-oriented: e.g. customers, patients, e.g. customers, patients, students, productsstudents, products

Integrated: Integrated: Consistent naming conventions, Consistent naming conventions, formats, encoding structures; from multiple data formats, encoding structures; from multiple data sourcessources

Time-variant: Time-variant: Can study trends and changesCan study trends and changes Nonupdatable: Nonupdatable: Read-only, periodically refreshedRead-only, periodically refreshed

Data MartData Mart:: A data warehouse that is limited in scopeA data warehouse that is limited in scope


Need for Data WarehousingNeed for Data Warehousing Integrated, company-wide view of high-quality Integrated, company-wide view of high-quality

information (from disparate databases)information (from disparate databases) Separation of Separation of operationaloperational and and informationalinformational

systems and data (for improved performance)systems and data (for improved performance)


Source: adapted from Strange (1997).


Data Warehouse Data Warehouse ArchitecturesArchitectures

Generic Two-Level ArchitectureGeneric Two-Level Architecture Independent Data MartIndependent Data Mart Dependent Data Mart and Dependent Data Mart and

Operational Data StoreOperational Data Store Logical Data Mart and Real-Time Data Logical Data Mart and Real-Time Data

WarehouseWarehouse Three-Layer architectureThree-Layer architecture

All involve some form of extraction, transformation and loading (ETLETL)


Figure 11-2: Generic two-level data warehousing architecture

E

T

LOne, company-wide warehouse

Periodic extraction data is not completely current in warehouse


Figure 11-3 Independent data mart data warehousing architecture

Data marts: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


Figure 11-4 Dependent data mart with operational data store: a three-level architecture

ET

L

Single ETL for enterprise data warehouse(EDW)(EDW)

Simpler data access

ODS ODS provides option for obtaining current data

Dependent data marts loaded from EDW


ET

L

Near real-time ETL for Data WarehouseData Warehouse

ODS ODS and data warehousedata 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


Figure 11-6 Three-layer data architecture for a data warehouse


Data CharacteristicsData CharacteristicsStatus vs. Event DataStatus vs. Event Data

Status

Status

Event = a database action (create/update/delete) that results from a transaction

Figure 11-7 Example of DBMS

log entry


Data CharacteristicsData CharacteristicsTransient vs. Periodic DataTransient 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


Periodic data are

never physicall

y altered

or deleted

once they have been

added to the store

Data CharacteristicsData CharacteristicsTransient vs. Periodic DataTransient vs. Periodic Data

Figure 11-9: Periodic

warehouse data


Other Data Warehouse Other Data Warehouse ChangesChanges

New descriptive attributesNew descriptive attributes New business activity attributesNew business activity attributes New classes of descriptive attributesNew classes of descriptive attributes Descriptive attributes become more Descriptive attributes become more

refinedrefined Descriptive data are related to one Descriptive data are related to one

anotheranother New source of dataNew source of data


The Reconciled Data LayerThe Reconciled Data Layer Typical operational data is:Typical operational data is:

Transient–not historicalTransient–not historical Not normalized (perhaps due to denormalization for Not normalized (perhaps due to denormalization for

performance)performance) Restricted in scope–not comprehensiveRestricted in scope–not comprehensive Sometimes poor quality–inconsistencies and errorsSometimes poor quality–inconsistencies and errors

After ETL, data should be:After ETL, data should be: Detailed–not summarized yetDetailed–not summarized yet Historical–periodicHistorical–periodic Normalized–3Normalized–3rdrd normal form or higher normal form or higher Comprehensive–enterprise-wide perspectiveComprehensive–enterprise-wide perspective Timely–data should be current enough to assist decision-Timely–data should be current enough to assist decision-

makingmaking Quality controlled–accurate with full integrityQuality controlled–accurate with full integrity


The ETL ProcessThe ETL Process

Capture/ExtractCapture/Extract Scrub or data cleansingScrub or data cleansing TransformTransform Load and IndexLoad and Index

ETL = Extract, transform, and load


Static extractStatic extract = capturing a snapshot of the source data at a point in time

Incremental extractIncremental 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


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


(cont.)


Transform = convert data from format of operational system to format of data warehouse

Record-level:Record-level:Selection–data partitioningJoining–data combiningAggregation–data summarization

Field-level:Field-level: single-field–from one field to one fieldmulti-field–from many fields to one, or one field to many


(cont.)


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


(cont.)


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


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


Derived DataDerived Data ObjectivesObjectives

Ease of use for decision support applicationsEase of use for decision support applications Fast response to predefined user queriesFast response to predefined user queries Customized data for particular target audiencesCustomized data for particular target audiences Ad-hoc query supportAd-hoc query support Data mining capabilitiesData mining capabilities

CharacteristicsCharacteristics Detailed (mostly periodic) dataDetailed (mostly periodic) data Aggregate (for summary)Aggregate (for summary) Distributed (to departmental servers)Distributed (to departmental servers)

Most common data model = star schemastar schema(also called “dimensional model”)


Figure 11-13 Components of a star schemastar schemaFact 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


Figure 11-14 Star schema example

Fact table provides statistics for sales broken down by product, period and store dimensions


Figure 11-15 Star schema with sample data


Issues Regarding Star Issues Regarding Star SchemaSchema

Dimension table keys must be Dimension table keys must be surrogatesurrogate (non- (non-intelligent and non-business related), because:intelligent and non-business related), because: Keys may change over timeKeys may change over time Length/format consistencyLength/format consistency

Granularity of Fact Table–what level of detail do Granularity of Fact Table–what level of detail do you want? you want? Transactional grain–finest levelTransactional grain–finest level Aggregated grain–more summarizedAggregated grain–more summarized Finer grains Finer grains better better market basket analysismarket basket analysis capability capability Finer grain Finer grain more dimension tables, more rows in fact table more dimension tables, more rows in fact table

Duration of the database–how much history Duration of the database–how much history should be kept?should be kept? Natural duration–13 months or 5 quartersNatural duration–13 months or 5 quarters Financial institutions may need longer durationFinancial institutions may need longer duration Older data is more difficult to source and cleanseOlder data is more difficult to source and cleanse


Figure 11-16: Modeling dates

Fact tables contain time-period data Date dimensions are important


The User InterfaceThe User InterfaceMetadata (data catalog)Metadata (data catalog)

Identify subjects of the data martIdentify subjects of the data mart Identify dimensions and factsIdentify dimensions and facts Indicate how data is derived from enterprise Indicate how data is derived from enterprise

data warehouses, including derivation rulesdata warehouses, including derivation rules Indicate how data is derived from operational Indicate how data is derived from operational

data store, including derivation rulesdata store, including derivation rules Identify available reports and predefined queriesIdentify available reports and predefined queries Identify data analysis techniques (e.g. drill-down)Identify data analysis techniques (e.g. drill-down) Identify responsible peopleIdentify responsible people


On-Line Analytical Processing (OLAP) On-Line Analytical Processing (OLAP) ToolsTools

The use of a set of graphical tools that provides The use of a set of graphical tools that provides users with multidimensional views of their data users with multidimensional views of their data and allows them to analyze the data using and allows them to analyze the data using simple windowing techniquessimple windowing techniques

Relational OLAP (ROLAP)Relational OLAP (ROLAP) Traditional relational representationTraditional relational representation

Multidimensional OLAP (MOLAP)Multidimensional OLAP (MOLAP) CubeCube structure structure

OLAP OperationsOLAP Operations Cube slicingCube slicing–come up with 2-D view of data–come up with 2-D view of data Drill-downDrill-down–going from summary to more detailed –going from summary to more detailed

viewsviews


Figure 11-23 Slicing a data cube


Figure 11-24 Example of drill-down

Summary report

Drill-down with color added

Starting with summary data, users can obtain details for particular cells


Data Mining and Data Mining and VisualizationVisualization

Knowledge discovery using a blend of statistical, AI, and computer Knowledge discovery using a blend of statistical, AI, and computer graphics techniquesgraphics techniques

Goals:Goals: Explain observed events or conditionsExplain observed events or conditions Confirm hypothesesConfirm hypotheses Explore data for new or unexpected relationshipsExplore data for new or unexpected relationships

TechniquesTechniques Statistical regressionStatistical regression Decision tree inductionDecision tree induction Clustering and signal processingClustering and signal processing AffinityAffinity Sequence associationSequence association Case-based reasoningCase-based reasoning Rule discoveryRule discovery Neural netsNeural nets FractalsFractals

Data visualization–representing data in graphical/multimedia formats Data visualization–representing data in graphical/multimedia formats for analysisfor analysis

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