tuesday introduction to olap and dimensional modelling

8/12/2019 Tuesday Introduction to OLAP and Dimensional Modelling

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Introduction to OLAP and

Dimensional Modelling

Tuesday


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Overview: Tuesday

Format Time Description

Lecture 10:00 - 10:45 Introduction to OLAP andAnalysis Services

Demo 10:45 - 11:30 Dimensional modelling

Lab 12:15 - 13:00 Practical session: Defining adata source and defining anddeploying a cube

Lab 13:00 - 13:45 Practical session: Modifyingmeasures, attributes and

hierarchiesLecture 14:30 - 15:15 Observations about design forOLAP and Reporting

Discussion 15:15 - 16:00 Wrap-up: questions andfeedback


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Definition of OLAP

Fast Analysis of Shared Multidimensional

Information (FASMI, Nigel Pendse)

Fast

Analysis (statistical and business logic)

Shared

Multidimensional

Information (all of the data and derived

information needed)


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Multidimensional

The system must provide a

multidimensional conceptual view of the

data, including full support for hierarchies

and multiple hierarchies, as this is

certainly the most logical way to analyze

businesses and organizations.


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Alternative definition of OLAP

(from SAS)

OLAP is "fast access to large amounts ofsummarized data".

This implies the concept of dimensionality. For

without dimensions, there would be nothing tosummarize the data by.

Alternative definition is that OLAP provides:

"the ability of users to conveniently interrogate

large amounts of data, at varying levels of detail,across a variety of combinations of businessdimensions"


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Kimballs Four-Step Design

Process

1. Select a business process

2. Declare the grain

3. Choose dimensions4. Identify facts


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STEP 1: Select a business process

For our exercise, we will be looking at

Internet sales


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A Quick Look at the Data (1)

USE AdventureWorksDW;

SELECT TOP 5

CustomerKey,ProductKey, OrderDateKey,

OrderQuantity

FROM FactInternetSales


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A Quick Look at the Data (2)

Customer Product OrderDate SalesAmount

Key Key Key

----------- ---------- ------------ ---------------------

11003 346 1 3399.99

14501 336 1 699.0982

21768 310 1 3578.27

25863 346 1 3399.99

28389 346 1 3399.99


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STEP 2: Declare the grain

(What does a row in the fact table mean?)

In our example, a row is an individual

order.

Design rule: recognise the trade-off.

A finer grain facilitates more detailed analysis,

but results in a larger quantity of data.


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STEP 4: Identify facts

The numeric facts that we will measure

FactInternetSales


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DimCustomer

CustomerKey

GeographyKey

CustomerAlternateKey

Title

FirstName

MiddleName

DimGeography

GeographyKey

City

StateProvinceCode

StateProvinceName

CountryRegionCode

EnglishCountryRegionName

SpanishCountryRegionNa...

DimProduct

ProductKey

ProductAlternateKey

ProductSubcategoryKey

WeightUnitMeasureCode

SizeUnitMeasureCode

EnglishProductName

SpanishProductName

FrenchProductName

StandardCost

FinishedGoodsFlag

Color

SafetyStockLevel

ReorderPoint

ListPrice

DimTime

TimeKey

FullDateAlternateKey

DayNumberOfWeek

EnglishDayNameOfWeek

SpanishDayNameOfWeek

FrenchDayNameOfWeek

DayNumberOfMonth

FactInternetSales

ProductKey

OrderDateKey

DueDateKey

ShipDateKey

CustomerKey

PromotionKey

CurrencyKey

SalesTerritoryKey


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Demo

Dimensional modelling


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Lab

Defining a data source view

Defining and deploying a cube


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Lab

Practical session: Modifying measures,

attributes and hierarchies


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Lecture: Observations about design

for OLAP and Reporting


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The BI Bottleneck (1)

Report consumers The report may be electronic, e.g. Excel

Power users Capable of some self-service

Report authors The know the data and the business.

Reporting administrator They know the database and the data, but not necessarily how it

relates to the business.

Challenge: make reporting more interactive so thatchanges can be accommodated without passing alongthe chain


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The BI bottleneck (2)

Typically, analysts time is the scarce

resource.

The number of iterations is the killer.

Sometimes, testing is the bottleneck.

Possible solution: the analyst spends a bit

more time in the first iteration providing the

business user with a more

generic/interactive report.


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The BI Bottleneck (3)

Long lead times

High development costs

Apparently small changes to arequirement for a report take a long time to

implement.

For each link along the chain that arequest for a change needs to go, delay

goes up by a big factor.


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The Relational Model of Data

Conceptually, homogeneous tabular structure:

Logic: for declarative query language

Algebra: for query optimization

Application interface (e.g. simple reporting tools). Applications designers and even some end-users can

(just about) understand tables.

Relational model provided a mutually intelligible

language for implementers, administrators,developers, researchers and even users.

Flexible: join anything with anything (c.f. OLAP).


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Inadequacy of the Relational Model

for Reporting applications

Heterogeneous data sources:

Database, OLAP, XML Web services, etc.

Relational model does not fit well with the

area between storage and presentation.

Aggregation hierarchies

Matrix structures


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The Microsoft approach: UDM

Server Analysis Services 2005 implements

UDM.

Acts as a bridge between users and their data.

Encapsulates semantics, language and time.

UDM perspectives allow the user to view

subsets.

Integrated with Data Mining. Accessed via SOAP and XML for Analysis.


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UDM

A UDM provides a single dimensional model for allOLAP analysis andrelational reporting needs. So you can use either MDX or SQL

Perspectives are the new data marts

Cubes are largely transparent concepts downgraded to the status of caches

Commonly youll only have 1 cube with multiple measure groups andmultiple perspectives.

Its better to think of measure groups instead of cubes; partitions now applyto measure groups.

Whilst a UDM can gather data from numerous data sources, the need tocleanse data still requires a data warehouse.

A cube is structured around dimensional attributes(previously known asmember properties) rather than dimensional hierarchies. Hence the virtualdimension, as a term, is now gone and concept converted to a real, firstclass, dimension.

UDM has five new dimension types, Role Playing, Fact, Reference, DataMining and Many to many.


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The pre-UDM and UDM stacks

Pre-UDM stack

Dimension model

(pivot table)

Calculations (Excel) End-user model (if you

are lucky)

Data source view

Management settings

UDM stack

Management settings

End-user model

Calculations Dimensional model

Data source view


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Enterprise BI with UDM

DW

Datamart

Datamart

BI Applications

XML

Web

Service

MOLAP

Reporting

Tool (1)

OLAP

Browser (2)

OLAP

Browser (1)

Reporting

Tool (1)UDM


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Desirable features of a BI data

model

The model must facilitate

re-use of report spare-parts by the power

users (rather than just the report authors).

more flexibility for report consumers

easier maintenance of the set of all reports

used by an enterprise. (E.g. Avoiding the

reporting chain.) Interaction.


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Current design principles

All about how to make reports look good.

See, for example, Microsoft SQL Server 2005

Report Design: Best Practices and Guidelines

Some focus on maintenance.

No focus on re-use.


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Wrap-Up

Questions

Feedback

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