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Accessing the Data Warehouse

Accessing the Data warehouse.ppt

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Accessing the Data

Warehouse

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Introduction

Purpose of building a DW

To provide user access to a set of data

Requires Tools

Aim

Aid the readers to understand accessing DW and

the major issues associated with it

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Choosing an access tool

Golden rules

 Never design the DW to suit a specific tool

Make no assumptions about the type of tool that

will be used

DW must be designed for 

Efficiency of query access

Ease of management

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 Types of Tool

Data dipping

Basic business tools

Generates standard business reports

Data mining

Specialist tools designed for finding trends and

 patterns

Data analysis

Performs complex analysis of data

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Data dipping tools

Perform basic analysis

Answers standard business questions

What were the sales last month?

How many new customers joined last month?

Have reasonable drill-down capabilities

Use meta data to present a business friendly

schema to the user 

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Data mining tools

Use techniques like AI and NN

Identifies common behavioral trends

Customer buying pattern, customer groups to root

out market segmentss

Users of this tool

Analyst who have good knowledge of the business

data

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Data analysis tools

Performs sophisticated analysis

Understand common business metrics

Market share, profitability

Types

ROLAPTraditional SQL-oriented tools that have tightintegration to the relational model

MOLAP

Tools that use matrix arithmetic and sparse matrixoptimization

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Multidimensional analysis

Data can be analyzed in many dimensions

Advantage of MD tools

Fast on predefined set of data

Operations like time series analysis, top-ten and

 bottom-ten selection are faster 

Dimensional slicing is good

Disadvantages

Loading the cube is very slow

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Key periods

Important reporting and financial periods

Does your business require short term analysis or long term

Key business dates Need to be documented

Set of holidays, Start of company’s business year, Start of the local taxyear 

Business times

Business week day working hours, Weekend working practices,Starting day of business week 

Capture all the time, period and business date information asthey crucially affect the DW design

Period information must be captured by department, group and

many other organizational divisions

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Meaningful level of data

How much detailed information is required on each key

dimension?

What level of details does data need to go?

Document the answers

Get the level of detail at which data must be stored

correctIf this decision is made incorrectly the DW will need

to be completely reorganized

Having understood all these details willRound out the query requirements

Query sort estimates can be calculated

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Query categories

Batch reports

Well understood requirementsRun offline and scheduled around any daily access andovernight process

Canned queriesPredefined queries

Run online

Data set size they access vary

Ad hoc queriesUnpredictable elements

DW must run any query when desired and expect areasonable response

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Designing to the query requirements

To make a DW to be query efficient even for 

ad hoc access

Star schemas

Denormalization

Query parallelism

Data partitioning

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Avoiding inefficient queries

To avoid query occupying the entire resource and

that runs for ever Use profiles

 –  Limits the amount of resource a user process can use

 –  If the limit exits stop processing the query

 –  DISADV: can be build only after wasting lot of resource

Queuing of queries

 –  All queries must be submitted via query manger 

» Degree of parallelism increases

» Other resources of the query can be controlled