Advantages and disadvantages of data warehouse ppt

Chapter 3:

Data Warehousing

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

(Continued…)

Understand the basic definitions and concepts of data warehouses

Learn different types of data warehousing architectures; their comparative advantages and disadvantages

Describe the processes used in developing and managing data warehouses

Explain data warehousing operations

…

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Learning Objectives

Explain the role of data warehouses in decision support

Explain data integration and the extraction, transformation, and load (ETL) processes

Describe real-time (a.k.a. right-time and/or active) data warehousing

Understand data warehouse administration and security issues

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Opening Vignette…

“Isle of Capri Casinos Is Winning with Enterprise Data Warehouse”

Company background

Problem description

Proposed solution

Results

Answer & discuss the case questions.

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Questions for the Opening Vignette

Why is it important for Isle to have an EDW?

What were the business challenges or opportunities that Isle was facing?

What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development.

What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case?

Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure?

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Main Data Warehousing Topics

DW definition

Characteristics of DW

Data Marts

ODS, EDW, Metadata

DW Framework

DW Architecture & ETL Process

DW Development

DW Issues

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What is a Data Warehouse?

A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format

“The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”

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A Historical Perspective to Data Warehousing

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Characteristics of DWs

Subject oriented

Integrated

Time-variant (time series)

Nonvolatile

Summarized

Not normalized

Metadata

Web based, relational/multi-dimensional

Client/server, real-time/right-time/active...

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Data Mart

A departmental small-scale “DW” that stores only limited/relevant data

Dependent data mart

A subset that is created directly from a data warehouse

Independent data mart

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

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Other DW Components

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

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Application Case 3.1

A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry

Questions for Discussion

What are the main challenges for TELCOs?

How can data warehousing and data analytics help TELCOs in overcoming their challenges?

Why do you think TELCOs are well suited to take full advantage of data analytics?

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A Generic DW Framework

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Application Case 3.2

Data Warehousing Helps MultiCare Save More Lives

Questions for Discussion

What do you think is the role of data warehousing in healthcare systems?

How did MultiCare use data warehousing to improve health outcomes?

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DW Architecture

Three-tier architecture

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 two tiers in three-tier architecture is combined into one

… sometimes there is only one tier?

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DW Architectures

3-tier

architecture

2-tier

architecture

1-tier

Architecture

?

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Data Warehousing Architectures

Issues to consider when deciding which architecture to use:

Which database management system (DBMS) should be used?

Will parallel processing and/or partitioning be used?

Will data migration tools be used to load the data warehouse?

What tools will be used to support data retrieval and analysis?

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A Web-Based DW Architecture

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Alternative DW Architectures

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Alternative DW Architectures

Each architecture has advantages and disadvantages!

Which architecture is the best?

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Ten factors that potentially affect the architecture selection decision

Information interdependence between organizational units

Upper management’s information needs

Urgency of need for a data warehouse

Nature of end-user tasks

Constraints on resources

Strategic view of the data warehouse prior to implementation

Compatibility with existing systems

Perceived ability of the in-house IT staff

Technical issues

Social/political factors

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Teradata Corp. DW Architecture

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Data Integration and the Extraction, Transformation, and Load Process

ETL = Extract Transform Load

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, such as relational or multidimensional databases, Web services, etc.

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Data Integration and the Extraction, Transformation, and Load Process

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ETL (Extract, Transform, Load)

Issues affecting the purchase of an ETL tool

Data transformation tools are expensive

Data transformation tools may have a long learning curve

Important criteria in selecting an ETL tool

Ability to read from and write to an unlimited number of data sources/architectures

Automatic capturing and delivery of metadata

A history of conforming to open standards

An easy-to-use interface for the developer and the functional user

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Data Warehouse Development

Data warehouse development approaches

Inmon Model: EDW approach (top-down)

Kimball Model: Data mart approach (bottom-up)

Which model is best?

Table 3.3 provides a comparative analysis between EDW and Data Mart approach

One alternative is the hosted warehouse

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Application Case 3.5

Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing

Questions for Discussion

How big and complex are the business operations of Starwood Hotels & Resorts?

How did Starwood Hotels & Resorts use data warehousing for better profitability?

What were the challenges, the proposed solution, and the obtained results?

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Additional DW Considerations Hosted Data Warehouses

Benefits:

Requires minimal investment in infrastructure

Frees up capacity on in-house systems

Frees up cash flow

Makes powerful solutions affordable

Enables solutions that provide for growth

Offers better quality equipment and software

Provides faster connections

… more in the book

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Representation of Data in DW

Dimensional Modeling

A retrieval-based system that supports high-volume query access

Star schema

The most commonly used and the simplest style of dimensional modeling

Contain a fact table surrounded by and connected to several dimension tables

Snowflakes schema

An extension of star schema where the diagram resembles a snowflake in shape

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The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)

Multidimensional presentation

Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry

Measures: money, sales volume, head count, inventory profit, actual versus forecast

Time: daily, weekly, monthly, quarterly, or yearly

Multidimensionality

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Star versus Snowflake Schema

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Analysis of Data in DW

OLTP vs. OLAP…

OLTP (online transaction processing)

Capturing and storing data from ERP, CRM, POS, …

The main focus is on efficiency of routine tasks

OLAP (Online analytical processing)

Converting data into information for decision support

Data cubes, drill-down / rollup, slice & dice, …

Requesting ad hoc reports

Conducting statistical and other analyses

Developing multimedia-based applications

…more in the book

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OLAP vs. OLTP

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OLAP Operations

Slice - a subset of a multidimensional array

Dice - a slice on more than two dimensions

Drill Down/Up - navigating among levels of data ranging from the most summarized (up) to the most detailed (down)

Roll Up - computing all of the data relationships for one or more dimensions

Pivot - used to change the dimensional orientation of a report or an ad hoc query-page display

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OLAP

Slicing Operations on a Simple Tree-Dimensional

Data Cube

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Variations of OLAP

Multidimensional OLAP (MOLAP)

OLAP implemented via a specialized multidimensional database (or data store) that summarizes transactions into multidimensional views ahead of time

Relational OLAP (ROLAP)

The implementation of an OLAP database on top of an existing relational database

Database OLAP and Web OLAP (DOLAP and WOLAP); Desktop OLAP,…

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Technology Insights 3.2 Hands-On DW with MicroStrategy

A wealth of teaching and learning resources can be found at TUN portal

www.teradatauniversitynetwork.com

The available resource includes scripted demonstrations, assignments, white papers, etc…

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DW Implementation Issues

Identification of data sources and governance

Data quality planning, data model design

ETL tool selection

Establishment of service-level agreements

Data transport, data conversion

Reconciliation process

End-user support

Political issues

… more in the book

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Successful DW Implementation Things to Avoid

Starting with the wrong sponsorship chain

Setting expectations that you cannot meet

Engaging in politically naive behavior

Loading the data warehouse with information just because it is available

Believing that data warehousing database design is the same as transactional database design

Choosing a data warehouse manager who is technology oriented rather than user oriented

… more in the book

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Failure Factors in DW Projects

Lack of executive sponsorship

Unclear business objectives

Cultural issues being ignored

Change management

Unrealistic expectations

Inappropriate architecture

Low data quality / missing information

Loading data just because it is available

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Massive DW and Scalability

Scalability

The main issues pertaining to scalability:

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 data-access functions will grow linearly with the size of the warehouse

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Real-Time/Active DW/BI

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

Push vs. Pull (of data)

Concerns about real-time BI

Not all data should be updated continuously

Mismatch of reports generated minutes apart

May be cost prohibitive

May also be infeasible

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Enterprise Decision Evolution and Data Warehousing

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Real-Time/Active DW at Teradata

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Traditional versus Active DW

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DW Administration and Security

Data warehouse administrator (DWA)

DWA should…

have the knowledge of high-performance software, hardware and networking technologies

possess solid business knowledge and insight

be familiar with the decision-making processes so as to suitably design/maintain the data warehouse structure

possess excellent communications skills

Security and privacy is a pressing issue in DW

Safeguarding the most valuable assets

Government regulations (HIPAA, etc.)

Must be explicitly planned and executed

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The Future of DW

Sourcing…

Web, social media, and Big Data

Open source software

SaaS (software as a service)

Cloud computing

Infrastructure…

Columnar

Real-time DW

Data warehouse appliances

Data management practices/technologies

In-database & In-memory processing New DBMS

Advanced analytics

…

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Free of Charge DW Portal for Teaching & Learning

www.TeradataStudentNetwork.com

Password to signup:

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End of the Chapter

Questions, comments

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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1970s1980s1990s2000s2010s

üMainframe computers

üSimple data entry

üRoutine reporting

üPrimitive database structures

üTeradata incorporated

üMini/personal computers (PCs)

üBusiness applications for PCs

üDistributer DBMS

üRelational DBMS

üTeradata ships commercial DBs

üBusiness Data Warehousecoined

üCentralized data storage

üData warehousing was born

üInmon, Building the Data Warehouse

üKimball, The Data Warehouse Toolkit

üEDW architecture design

üExponentially growing data Web data

üConsolidation of DW/BI industry

üData warehouse appliances emerged

üBusiness intelligence popularized

üData mining and predictive modeling

üOpen source software

üSaaS, PaaS, Cloud Computing

üBig Data analytics

üSocial media analytics

üText and Web Analytics

üHadoop, MapReduce, NoSQL

üIn-memory, in-database

Data

Sources

ERP

Legacy

POS

Other

OLTP/wEB

External

data

Select

Transform

Extract

Integrate

Load

ETL

Process

Enterprise

Data warehouse

Metadata

Replication

A

P

I

/

M

i

d

d

l

e

w

a

r

e

Data/text

mining

Custom built

applications

OLAP,

Dashboard,

Web

Routine

Business

Reporting

Applications

(Visualization)

Data mart

(Engineering)

Data mart

(Marketing)

Data mart

(Finance)

Data mart

(...)

Access

No data marts option

Tier 2:

Application server

Tier 1:

Client workstation

Tier 3:

Database server

Tier 1:

Client workstation

Tier 2:

Application & database server

Web

Server

Client

(Web browser)

Application

Server

Data

warehouse

Web pages

Internet/

Intranet/

Extranet

Source

Systems

Staging

Area

Independent data marts

(atomic/summarized data)

End user

access and

applications

ETL

Source

Systems

Staging

Area

End user

access and

applications

ETL

Dimensionalized data marts

linked by conformed dimensions

(atomic/summarized data)

Source

Systems

Staging

Area

End user

access and

applications

ETL

Normalized relational

warehouse (atomic data)

Dependent data marts

(summarized/some atomic data)

(a) Independent Data Marts Architecture

(b) Data Mart Bus Architecture with Linked Dimensional Datamarts

(c) Hub and Spoke Architecture (Corporate Information Factory)

Source

Systems

Staging

Area

Normalized relational

warehouse (atomic/some

summarized data)

End user

access and

applications

End user

access and

applications

Logical/physical integration of

common data elements

Existing data warehouses

Data marts and legacy systems

ETL

Data mapping / metadata

(d) Centralized Data Warehouse Architecture

(e) Federated Architecture

Packaged

application

Legacy

system

Other internal

applications

Transient

data source

ExtractTransformCleanseLoad

Data

warehouse

Data mart

Fact Table

SALES

UnitsSold

...

Dimension

TIME

Quarter

...

Dimension

PEOPLE

Division

...

Dimension

PRODUCT

Brand

...

Dimension

GEOGRAPHY

Country

...

Fact Table

SALES

UnitsSold

...

Dimension

DATE

Date

...

Dimension

PEOPLE

Division

...

Dimension

PRODUCT

LineItem

...

Dimension

STORE

LocID

...

Dimension

BRAND

Brand

...

Dimension

CATEGORY

Category

...

Dimension

LOCATION

State

...

Dimension

MONTH

M_Name

...

Dimension

QUARTER

Q_Name

...

Star SchemaSnowflake Schema

Product

T

i

m

e

G

e

o

g

r

a

p

h

y

Sales volumes of

a specific Product

on variable Time

and Region

Sales volumes of

a specific Region

on variable Time

and Products

Sales volumes of

a specific Time on

variable Region

and Products

Cells are filled

with numbers

representing

sales volumes

A 3-dimensional

OLAP cube with

slicing

operations