business intelligence and decision support...
TRANSCRIPT
Business Intelligence and Decision Support Systems
(9th Ed., Prentice Hall)
Chapter 8: Data Warehousing
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-2
Learning Objectives
n Understand the basic definitions and concepts of data warehouses
n Learn different types of data warehousing architectures; their comparative advantages and disadvantages
n Describe the processes used in developing and managing data warehouses
n Explain data warehousing operations n Explain the role of data warehouses in
decision support
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-3
Learning Objectives
n Explain data integration and the extraction, transformation, and load (ETL) processes
n Describe real-time (a.k.a. right-time and/or active) data warehousing
n Understand data warehouse administration and security issues
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-4
Opening Vignette:
“DirecTV Thrives with Active Data Warehousing”
n Company background n Problem description n Proposed solution n Results n Answer & discuss the case questions.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-5
Main Data Warehousing (DW) Topics
n DW definitions n Characteristics of DW n Data Marts n ODS, EDW, Metadata n DW Framework n DW Architecture & ETL Process n DW Development n DW Issues
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-6
Data Warehouse Defined
n A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
n “The data warehouse is a collection of integrated, subject-oriented databases design to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-7
Characteristics of DW
n Subject oriented n Integrated n Time-variant (time series) n Nonvolatile n Summarized n Not normalized n Metadata n Web based, relational/multi-dimensional n Client/server n Real-time and/or right-time (active)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-8
Data Mart
A departmental data warehouse that stores only relevant data
n Dependent data mart A subset that is created directly from a data warehouse
n Independent data mart A small data warehouse designed for a strategic business unit or a department
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-9
Data Warehousing Definitions
n Operational data stores (ODS) A type of database often used as an interim area for a data warehouse
n Oper marts An operational data mart.
n Enterprise data warehouse (EDW) A data warehouse for the enterprise.
n Metadata Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-10
A Conceptual Framework for DW
DataSources
ERP
Legacy
POS
OtherOLTP/wEB
External data
Select
Transform
Extract
Integrate
Load
ETL Process
EnterpriseData warehouse
Metadata
Replication
A P
I
/ M
iddl
ewar
e Data/text mining
Custom builtapplications
OLAP,Dashboard,Web
RoutineBusinessReporting
Applications(Visualization)
Data mart(Engineering)
Data mart(Marketing)
Data mart(Finance)
Data mart(...)
Access
No data marts option
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-11
Generic DW Architectures
n Three-tier architecture 1. Data acquisition software (back-end) 2. The data warehouse that contains the data &
software 3. Client (front-end) software that allows users to
access and analyze data from the warehouse
n Two-tier architecture First 2 tiers in three-tier architecture is combined
into one
… sometime there is only one tier?
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-12
Generic DW Architectures
Tier 2:Application server
Tier 1:Client workstation
Tier 3:Database server
Tier 1:Client workstation
Tier 2:Application & database server
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-13
DW Architecture Considerations
n Issues to consider when deciding which architecture to use: n Which database management system (DBMS)
should be used? n Will parallel processing and/or partitioning be
used? n Will data migration tools be used to load the
data warehouse? n What tools will be used to support data
retrieval and analysis?
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-14
A Web-based DW Architecture
WebServer
Client(Web browser)
ApplicationServer
Datawarehouse
Web pages
Internet/Intranet/Extranet
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-15
Alternative DW Architectures
SourceSystems
Staging Area
Independent data marts(atomic/summarized data)
End user access and applications
ETL
(a) Independent Data Marts Architecture
SourceSystems
Staging Area
End user access and applications
ETLDimensionalized data marts
linked by conformed dimentions(atomic/summarized data)
(b) Data Mart Bus Architecture with Linked Dimensional Datamarts
SourceSystems
Staging Area
End user access and applications
ETL
Normalized relational warehouse (atomic data)
Dependent data marts(summarized/some atomic data)
(c) Hub and Spoke Architecture (Corporate Information Factory)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-16
Alternative DW Architectures
SourceSystems
Staging Area
Normalized relational warehouse (atomic/some
summarized data)
End user access and applications
ETL
(d) Centralized Data Warehouse Architecture
End user access and applications
Logical/physical integration of common data elements
Existing data warehousesData marts and legacy systmes
Data mapping / metadata
(e) Federated Architecture
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-17
Alternative DW Architectures
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-18
Which Architecture is the Best?
n Bill Inmon versus Ralph Kimball n Enterprise DW versus Data Marts approach
Empirical study by Ariyachandra and Watson (2006)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-19
Data Warehousing Architectures
1. Information interdependence between organizational units
2. Upper management’s information needs
3. Urgency of need for a data warehouse
4. Nature of end-user tasks 5. Constraints on resources
6. Strategic view of the data warehouse prior to implementation
7. Compatibility with existing systems
8. Perceived ability of the in-house IT staff
9. Technical issues 10. Social/political factors
Ten factors that potentially affect the architecture selection decision:
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-20
Enterprise Data Warehouse (by Teradata Corporation)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-21
Data Integration and the Extraction, Transformation, and Load (ETL) Process n Data integration
Integration that comprises three major processes: data access, data federation, and change capture.
n Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse
n Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources
n Service-oriented architecture (SOA) A new way of integrating information systems
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-22
Extraction, transformation, and load (ETL) process
Data Integration and the Extraction, Transformation, and Load (ETL) Process
Packaged application
Legacy system
Other internal applications
Transient data source
Extract Transform Cleanse Load
Datawarehouse
Data mart
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-23
ETL
n Issues affecting the purchase of and ETL tool n Data transformation tools are expensive n Data transformation tools may have a long
learning curve
n Important criteria in selecting an ETL tool n Ability to read from and write to an unlimited
number of data sources/architectures n Automatic capturing and delivery of metadata n A history of conforming to open standards n An easy-to-use interface for the developer and the
functional user
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-24
Benefits of DW n Direct benefits of a data warehouse
n Allows end users to perform extensive analysis n Allows a consolidated view of corporate data n Better and more timely information n Enhanced system performance n Simplification of data access
n Indirect benefits of data warehouse n Enhance business knowledge n Present competitive advantage n Enhance customer service and satisfaction n Facilitate decision making n Help in reforming business processes
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-25
Data Warehouse Development n Data warehouse development approaches
n Inmon Model: EDW approach (top-down) n Kimball Model: Data mart approach (bottom-up) n Which model is best?
n There is no one-size-fits-all strategy to DW
n One alternative is the hosted warehouse
n Data warehouse structure: n The Star Schema vs. Relational
n Real-time data warehousing?
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-26
DW Development Approaches
See Table 8.3 for details
(Inmon Approach) (Kimball Approach)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-27
DW Structure: Star Schema (a.k.a. Dimensional Modeling)
Claim Information
Driver Automotive
TimeLocation
Start Schema Example for anAutomobile Insurance Data Warehouse
Dimensions:How data will be sliced/diced (e.g., by location, time period, type of automobile or driver)
Facts:Central table that contains (usually summarized) information; also contains foreign keys to access each dimension table.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-28
Dimensional Modeling
Data cube A two-dimensional, three-dimensional, or higher-dimensional object in which each dimension of the data represents a measure of interest - Grain - Drill-down - Slicing
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-29
Best Practices for Implementing DW
n The project must fit with corporate strategy n There must be complete buy-in to the project n It is important to manage user expectations n The data warehouse must be built incrementally n Adaptability must be built in from the start n The project must be managed by both IT and
business professionals (a business–supplier relationship must be developed)
n Only load data that have been cleansed/high quality n Do not overlook training requirements n Be politically aware.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-30
Risks in Implementing DW
n No mission or objective n Quality of source data unknown n Skills not in place n Inadequate budget n Lack of supporting software n Source data not understood n Weak sponsor n Users not computer literate n Political problems or turf wars n Unrealistic user expectations
(Continued …)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-31
Risks in Implementing DW – Cont.
n Architectural and design risks n Scope creep and changing requirements n Vendors out of control n Multiple platforms n Key people leaving the project n Loss of the sponsor n Too much new technology n Having to fix an operational system n Geographically distributed environment n Team geography and language culture
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-32
Things to Avoid for Successful Implementation of DW
n Starting with the wrong sponsorship chain n Setting expectations that you cannot meet n Engaging in politically naive behavior n Loading the warehouse with information just
because it is available n Believing that data warehousing database
design is the same as transactional DB design n Choosing a data warehouse manager who is
technology oriented rather than user oriented (…see more on page 356)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-33
Real-time DW (a.k.a. Active Data Warehousing)
n Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly n Push vs. Pull (of data)
n Concerns about real-time BI n Not all data should be updated continuously n Mismatch of reports generated minutes apart n May be cost prohibitive n May also be infeasible
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-34
Evolution of DSS & DW
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-35
Active Data Warehousing (by Teradata Corporation)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-36
Comparing Traditional and Active DW
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-37
Data Warehouse Administration
n Due to its huge size and its intrinsic nature, a DW requires especially strong monitoring in order to sustain its efficiency, productivity and security.
n The successful administration and management of a data warehouse entails skills and proficiency that go past what is required of a traditional database administrator. n Requires expertise in high-performance software,
hardware, and networking technologies
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-38
DW Scalability and Security n Scalability
n The main issues pertaining to scalability: n The amount of data in the warehouse n How quickly the warehouse is expected to grow n The number of concurrent users n The complexity of user queries
n Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse
n Security n Emphasis on security and privacy
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-39
BI / OLAP Portal for Learning n MicroStrategy, and much more… n www.TeradataStudentNetwork.com n Password: [**Keyword**]
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 8-40
End of the Chapter
n Questions, comments