Turban Dss9e Ch08

Chapter 8: Data Warehousing

Mohamad Ivan Fanany, PhD

DECISION SUPPORT AND BUSINESS INTELLIGENCE SYSTEMS

LEARNING OBJECTIVES Understand the basic definitions and concepts of data warehouses Ò  Learn different types of data warehousing architectures; their comparative advantages and disadvantages Ò  Describe the processes used in developing and managing data warehouses Ò  Explain data warehousing operations Ò  Explain the role of data warehouses in decision support Ò 

LEARNING OBJECTIVES Explain data integration and the extraction, transformation, and load (ETL) processes Ò  Describe real-time (a.k.a. right-time and/or active) data warehousing Ò  Understand data warehouse administration and security issues Ò 

MAIN DATA WAREHOUSING (DW) TOPICS Ò  DW

definitions Ò  Characteristics of DW Ò  Data Marts Ò  ODS, EDW, Metadata Ò  DW Framework Ò  DW Architecture & ETL Process Ò  DW Development Ò  DW Issues

DATA WAREHOUSE DEFINED Ò 

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A physical repository where relational data are specially organized to provide enterprisewide, cleansed data in a standardized format “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”

CHARACTERISTICS OF DW Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò 

Subject oriented Integrated Time-variant (time series) Nonvolatile Summarized Not normalized Metadata Web based, relational/multi-dimensional Client/server Real-time and/or right-time (active)

DATA MART A departmental data warehouse that stores only relevant data É  Dependent

data mart A subset that is created directly from a data warehouse

É  Independent

data mart A small data warehouse designed for a strategic business unit or a department

DATA WAREHOUSING DEFINITIONS Ò 

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Ò 

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Operational data stores (ODS) A type of database often used as an interim area for a data warehouse Oper marts An operational data mart. Enterprise data warehouse (EDW) A data warehouse for the enterprise. Metadata Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

A CONCEPTUAL FRAMEWORK FOR DW No data marts option Applications (Visualization)

Data Sources Access ETL Process Select Legacy

Metadata Extract

POS

Transform

Enterprise Data warehouse

Integrate Other OLTP/wEB

Data mart (Finance)

Load Replication

External data

Data mart (Engineering)

Data mart (...)

/ Middleware

Data mart (Marketing)

API

ERP

Routine Business Reporting

Data/text mining

OLAP, Dashboard, Web

Custom built applications

GENERIC DW ARCHITECTURES Ò 

Three-tier architecture 1.  2.  3. 

Ò 

Data acquisition software (back-end) The data warehouse that contains the data & software Client (front-end) software that allows users to access and analyze data from the warehouse

Two-tier architecture First 2 tiers in three-tier architecture is combined into one

… sometime there is only one tier?

GENERIC DW ARCHITECTURES

Tier 1: Client workstation

Tier 1: Client workstation

Tier 2: Application server

Tier 2: Application & database server

Tier 3: Database server

DW ARCHITECTURE CONSIDERATIONS Ò  Issues

to consider when deciding which architecture to use: É  Which

database management system (DBMS) should be used? É  Will parallel processing and/or partitioning be used? É  Will data migration tools be used to load the data warehouse? É  What tools will be used to support data retrieval and analysis?

A WEB-BASED DW ARCHITECTURE

Web pages

Client (Web browser)

Internet/ Intranet/ Extranet

Application Server

Web Server

Data warehouse

ALTERNATIVE DW ARCHITECTURES (a) Independent Data Marts Architecture ETL Source Systems

Staging Area

Independent data marts (atomic/summarized data)

End user access and applications

(b) Data Mart Bus Architecture with Linked Dimensional Datamarts ETL Source Systems

Staging Area

Dimensionalized data marts linked by conformed dimentions (atomic/summarized data)

End user access and applications

(c) Hub and Spoke Architecture (Corporate Information Factory) ETL Source Systems

Staging Area

Normalized relational warehouse (atomic data)

End user access and applications

Dependent data marts (summarized/some atomic data)

ALTERNATIVE DW ARCHITECTURES (d) Centralized Data Warehouse Architecture ETL Source Systems

Staging Area

Normalized relational warehouse (atomic/some summarized data)

End user access and applications

(e) Federated Architecture Data mapping / metadata Existing data warehouses Data marts and legacy systmes

Logical/physical integration of common data elements

End user access and applications

ALTERNATIVE DW ARCHITECTURES

WHICH ARCHITECTURE IS THE BEST? Bill Inmon versus Ralph Kimball Ò  Enterprise DW versus Data Marts approach Ò 

Empirical study by Ariyachandra and Watson (2006)

DATA WAREHOUSING ARCHITECTURES Ten factors that potentially affect the architecture selection decision: 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

ENTERPRISE DATA WAREHOUSE (BY TERADATA CORPORATION)

DATA INTEGRATION AND THE EXTRACTION, TRANSFORMATION, AND LOAD (ETL) PROCESS Ò 

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Data integration Integration that comprises three major processes: data access, data federation, and change capture. Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources Service-oriented architecture (SOA) A new way of integrating information systems

DATA INTEGRATION AND THE EXTRACTION, TRANSFORMATION, AND LOAD (ETL) PROCESS Extraction, transformation, and load (ETL) process Transient data source

Packaged application

Data warehouse Legacy system

Extract

Transform

Cleanse

Load

Data mart Other internal applications

ETL Ò 

Issues affecting the purchase of and ETL tool Data transformation tools are expensive É  Data transformation tools may have a long learning curve É 

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Important criteria in selecting an ETL tool Ability to read from and write to an unlimited number of data sources/architectures É  Automatic capturing and delivery of metadata É  A history of conforming to open standards É  An easy-to-use interface for the developer and the functional user É 

BENEFITS OF DW Ò 

Direct benefits of a data warehouse É  É  É  É  É 

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Allows end users to perform extensive analysis Allows a consolidated view of corporate data Better and more timely information Enhanced system performance Simplification of data access

Indirect benefits of data warehouse É  É  É  É  É 

Enhance business knowledge Present competitive advantage Enhance customer service and satisfaction Facilitate decision making Help in reforming business processes

DATA WAREHOUSE DEVELOPMENT n 

Data warehouse development approaches n  n  n 

Inmon Model: EDW approach (top-down) Kimball Model: Data mart approach (bottom-up) Which model is best? n 

n  n 

One alternative is the hosted warehouse

Data warehouse structure: n 

n 

There is no one-size-fits-all strategy to DW

The Star Schema vs. Relational

Real-time data warehousing?

DW DEVELOPMENT APPROACHES (Inmon Approach)

(Kimball Approach)

See Table 8.3 for details

DW STRUCTURE: STAR SCHEMA (A.K.A. DIMENSIONAL MODELING) Start Schema Example for an Automobile Insurance Data Warehouse Driver

Dimensions: How data will be sliced/ diced (e.g., by location, time period, type of automobile or driver)

Location

Automotive

Claim Information

Facts: Central table that contains (usually summarized) information; also contains foreign keys to access each dimension table.

Time

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

BEST PRACTICES FOR IMPLEMENTING DW Ò  Ò  Ò  Ò  Ò  Ò 

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The project must fit with corporate strategy There must be complete buy-in to the project It is important to manage user expectations The data warehouse must be built incrementally Adaptability must be built in from the start The project must be managed by both IT and business professionals (a business–supplier relationship must be developed) Only load data that have been cleansed/high quality Do not overlook training requirements Be politically aware.

RISKS IN IMPLEMENTING DW Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò 

No mission or objective Quality of source data unknown Skills not in place Inadequate budget Lack of supporting software Source data not understood Weak sponsor Users not computer literate Political problems or turf wars Unrealistic user expectations (Continued …)

RISKS IN IMPLEMENTING DW – CONT. Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò  Ò 

Architectural and design risks Scope creep and changing requirements Vendors out of control Multiple platforms Key people leaving the project Loss of the sponsor Too much new technology Having to fix an operational system Geographically distributed environment Team geography and language culture

THINGS TO AVOID FOR SUCCESSFUL IMPLEMENTATION OF DW Starting with the wrong sponsorship chain Ò  Setting expectations that you cannot meet Ò  Engaging in politically naive behavior Ò  Loading the warehouse with information just because it is available Ò  Believing that data warehousing database design is the same as transactional DB design Ò  Choosing a data warehouse manager who is technology oriented rather than user oriented (…see more on page 356) Ò 

REAL-TIME DW (A.K.A. ACTIVE DATA WAREHOUSING) Ò  Enabling

real-time data updates for real-time analysis and real-time decision making is growing rapidly É  Push

vs. Pull (of data)

Ò  Concerns

about real-time BI

Not all data should be updated continuously É  Mismatch of reports generated minutes apart É  May be cost prohibitive É  May also be infeasible É 

EVOLUTION OF DSS & DW

Active Data Warehousing (by Teradata Corporation)

COMPARING TRADITIONAL AND ACTIVE DW

DATA WAREHOUSE ADMINISTRATION Due to its huge size and its intrinsic nature, a DW requires especially strong monitoring in order to sustain its efficiency, productivity and security. Ò  The successful administration and management of a data warehouse entails skills and proficiency that go past what is required of a traditional database administrator. Ò 

É 

Requires expertise in high-performance software, hardware, and networking technologies

DW SCALABILITY AND SECURITY Ò 

Scalability É 

The main issues pertaining to scalability: Ð  Ð  Ð  Ð 

É 

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The amount of data in the warehouse How quickly the warehouse is expected to grow The number of concurrent users The complexity of user queries

Good scalability means that queries and other dataaccess functions will grow linearly with the size of the warehouse

Security É 

Emphasis on security and privacy

DATA WAREHOUSE VS DATABASE

DATA WAREHOUSE VS DATABASE

DATA WAREHOUSE VS DATAMART

NORMALIZATION IN DATA WAREHOUSE

DATA WAREHOUSE AND BIG DATA

WHAT CAN HADOOP DO THAT MY DATA WAREHOUSE CAN’T? (1) Store any and all kinds of data more cheaply and (2) Process all this data more quickly and cheaply.

WHY DO WE NEED HADOOP IF WE’RE NOT DOING BIG DATA?

WHY DO WE NEED HADOOP IF WE’RE NOT DOING BIG DATA? Ò  Contrary

to popular belief, Hadoop is not just for big data. (big data simply refers to data that doesn't fit comfortably – or at all – into our existing relational systems.) Ò  Hadoop was originally developed to address the big data needs of web/media companies, but today, it's being used around the world to address a wider set of data needs, big and small, by practically every industry.

IS HADOOP ENTERPRISE-READY? Ò  Hadoop

is not just another faster, more cost-effective engine option. It’s a game changer in the world of data management. Ò  It all depends on what and why you want to use Hadoop in your organization. If you simply want to use it as an additional (or alternative) storage repository and/or as a short-term data processor, then by all means, Apache Hadoop is ready for you.

IS HADOOP ENTERPRISE-READY? Ò  If

you want to go beyond data storage and processing, and you’re looking for some of the same data management and analysis capabilities you currently have with your existing data ecosystem, Apache Hadoop alone is not going to cut it. Ò  many of these newer Hadoop-related technologies are still maturing—quite rapidly.

ISN’T A DATA LAKE JUST THE DATA WAREHOUSE REVISITED?

WHAT ARE SOME OF THE PROS AND CONS OF A DATA LAKE?

END OF THE CHAPTER Ò  Questions

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