planning & project management

8/11/2019 Planning & Project Management

1/65

Prof. Chandan Singhavi


2/65

Requirements gathering

Requirements definition document with

information packages)

Data design

Dimensional model


3/65


4/65

Choosing the process Selecting the subjects from the information pakages for

the first set of logical structures to be designed

Choosing the grain Determining the level of detail for the data in the data

structures

Identifying and conforming dimensions Making sure that each particular data element in every

business dimension is conformed to one another

Choosing the facts Selecting the metrics or units of measures (eg, product

sales unit, dollar sales, dollar revenue) to be included infirst set

Choosing the duration of the database Determining how far back in time you should go for

historical data.


5/65

It is logical design technique to structure thebusiness dimensions and metrics that areanalyzed along these techniques

Gets its name form business dimensions

The model has also proved to provide high

performance for queries and analysis

Information Package is the foundation


6/65

Reviewing the information package diagram,we notice three types of entities

Measurements or metrics

Business dimensions

Attributes for each business dimension


7/65


8/65

It represents business dimensions Facts are used for the analysis

The attributes in the dimension table acts as filtersin our queries

Each dimension table has an equal chance of aquery

Each dimension table has direct relationship withthe fact table in the middle

Each dimension table has one to many relationship

with the fact table Such organization looks like a STAR


9/65


10/65

Dimension modeling should primarilyfacilitate queries and analysis

Typical query could be

How much sales proceeds did the jeepcherokee, year 2000 model with standardoptions, generate in jan 2000 at big sam

autodealership for buyers who own theirhomes and who took three years leases,financed by diamler chrysler financing.


11/65

Some criteria for combining the tables intodimension model

Model should provide best data access Must be query centric Optimize for queries and analysis Must show the dimension tables interact with fact

table Should be structured in a way that every

dimension should interact equally to the facttable Should allow drilling down or rolling up along

dimension hierarchy


12/65


13/65


14/65

definition A simple database design in which dimensional

data are separated from fact or eventdata(describing individual businesstransactions).

Also known as dimension model Suitable for Ad-hoc queries Simplest star schema consists of one fact table

surrounded by many dimension tables

Fact table Contain factual or quantitative data about a

business such as Units sold, order booked etc. PK of fact table is composite of FK


15/65


16/65


17/65


18/65

Key component of dimension model is set ofdimension tables


19/65


20/65


21/65


22/65


23/65

STAR scheme is a relational model, it is not a

normalized model:

Easy for user to understand

Optimizes navigation

Most suitable for query processing

STARjoin and STARindex


24/65

Over time size of fact table goes onincreasing -- may be new records or updates

Dimension table are more stable and lessvolatile


25/65

Slowly changing dimensions

Type 1 changes: correction of errors

Type 2 changes: preservation of history

Type 3 changes: tentative soft revisions


26/65

Most dimensions are constant over time Change slowly

Product key of source record does not change

Description and other attribute Changesslowly over the time

overwriting is not always appropriate


27/65

Principles Change relate to correction of errors

Change in the source system have nosignificance

Need not be preserve in the data warehouse


28/65


29/65

True changes in the source system Need to preserve history in the data

warehouse

Partitions the history in the data warehouse

Every change for the same attribute must bepreserve


30/65


31/65

They usually relate to soft or tentativechanges in the source system

There is a need to keep track of history withold and new values of the changed attribute

They are used to compare performanceacross the transition.

They provide the ability to track forward and

backward


32/65


33/65


34/65

Large dimensions, multiple hierarchies

Rapidly changing dimensions

Junk dimensions


35/65

Very deep or wide

Customer

Product


36/65

Need to address following issues by using effectivedesign methods, by choosing proper indexes and byapplying other optimization techniques

Population of very large dimension tables

Browse performance of unconstrained dimension,especially where the cardinality of the attribute is low

Browsing time for cross constrained values ofdimension attributes

Inefficiencies in fact table queries when largedimensions need to be used

Additional rows created to handle type 2 slowingchanging dimension


37/65


38/65

Dimension table could be littered with a verylarge number of additional rows created everytime there is an incremental load.

Effective approach is break the largedimension table may be separated into one ormore simpler dimension table.


39/65


40/65

Miscellaneous flags and textual field

Choices Exclude and discard all flags and texts. Place the flags and texts unchanged in the fact

table Make each flag and text a separate dimension

table on its own. Keep only those flags and texts that are

meaningful; group all the useful flags into a

single dimension junk These junk dimension attributes are useful for

constraining queries based on flag/text values.


41/65

Options to normalize

Advantages and disadvantages

When to snowflake


42/65

Snowflaking is a method of normalizing thedimension tables in a STAR schema.

When you completely normalize all the

dimension tables, the resultant structureresembles a snowflake with the fact table inthe middle.


43/65


44/65


45/65


46/65


47/65

Advantages Small saving in storage space

Normalized structures are easier to update andmaintain

Disadvantages Schema less intuitive and end users are put off by

the complexity

Ability to browse through the contents difficult Degraded query performance because of additionaljoins


48/65

Snow flaking is not generally recommendedin a data warehouse environment. Queryperformance takes highest priority


49/65

space Sub dimension


50/65


51/65


52/65


53/65


54/65


55/65

Tremendous boost to performance


56/65


57/65

Effect of sparsity on aggregation When you go for higher levels of aggregates, The

sparsity percentage moves up. You have to payattention to this problem

Aggregation option


58/65


59/65

Almost all data warehouses contain multiple

STAR scheme structures figure 11-16)

Snapshot and transaction tables figure 11-

17)

Core and custom tables figure 11-18)

Supporting enterprise value chain

Conforming dimensions,

standardizing facts


60/65


61/65


62/65


63/65

A conformed dimension is a comprehensivecombination of attributes from the sourcesystem after resolving all discrepancies andconflicts.

Confirm dimension allows rollup acrossdatamarts


64/65


65/65

planning & project management

Documents