datawarehouse in detail

8/18/2019 datawarehouse in detail

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SE-6C

Data Warehousing

ASSIGNMENT#2

HAMZA IMTIAZ MALIK !A"-$SE-%&'


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"( What is Hypercube in OLAP System?

Ans)er* An OLAP cube is a term that typically refers to multi-

dimensional array of data. OLAP is an acronym for onlineanalytical processing, which is a computer-based technique of analysing data to look for insights. The term cube here refers toa multi-dimensional dataset, which is also sometimes called ahypercube if the number of dimensions is greater than .A cube can be considered a multi-dimensional generali!ation of a two- or three-dimensional spreadsheet. "or e#ample, acompany might wish to summari!e $nancial data by product,by time-period, and by city to compare actual and budget

e#penses. Product, time, city and scenario %actual and budget&are the data's dimensions.(ube is a shortcut for multidimensional dataset, gi)en that datacan ha)e an arbitrary number of dimensions. The termhypercube is sometimes used, especially for data with morethan three dimensions.*licer is a term for a dimension which is held constant for allcells so that multidimensional information can be shown in atwo dimensional physical space of a spreadsheet or pi)ot table.

+ach cell of the cube holds a number that represents somemeasure of the business, such as sales, pro$ts, e#penses,budget and forecast.OLAP data is typically stored in a star schema or snowakeschema in a relational data warehouse or in a special-purposedata management system. easures are deri)ed from therecords in the fact table and dimensions are deri)ed from thedimension tables.

2( What is MULTI-DIMENSIONAL Anaysis+Ans)er*ulti-imensional Analysis is an /nformational Analysis on datawhich takes into account many di0erent relationships, each of which represents a dimension. "or e#ample, a retail analystmay want to understand the relationships among sales byregion, by quarter, by demographic distribution %income,education le)el, gender&, by product. ulti-dimensional analysiswill yield results for these comple# relationships.

ulti-imensional Analysis is generally used in statistics,econometrics and other related $elds and the results of this


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The dimension title member is the name of the member as inthe case of month or city. The dimension )alue member is aninstance of a dimension member. "or e#ample, 4556 is the)alue of the dimension )alue which is 7ear.

A data point refers to the intersection of multiple dimensionswhile a data )alue resides at the data point.ultidimensional analysis is )ery important in a businessenterprise because they are the basis for some of the decisionsof the business organi!ation which will gi)e them better edgeo)er the competitor. Today8s business en)ironment isconstantly e)ol)ing and business trends change )ery fast so itis always a good idea to analy!e enterprise related things.any software tools ha)e been de)eloped to make

multidimensional analysis processes a lot easier and faster. Amultidimensional analysis is often part of the larger businessintelligence system that works collaborati)ely with the datawarehouse system.

( $rie, .is/uss the DesignA00roa/hes 1 Ar/hite/ture DWH(

Ans)er*

Design A00roa/hes*i( $otto-30 Design*

/n the bottom-up design approach, the data marts are created$rst to pro)ide reporting capability. A data mart addresses asingle business area such as sales, "inance etc. These datamarts are then integrated to build a complete data warehouse.

The integration of data marts is implemented using datawarehouse bus architecture. /n the bus architecture, adimension is shared between facts in two or more data marts.

These dimensions are called conformed dimensions. These

conformed dimensions are integrated from data marts and thendata warehouse is built.Ad)antages of bottom-up design are9

This model contains consistent data marts and these datamarts can be deli)ered quickly.As the data marts are created $rst, reports can be generatedquickly.

The data warehouse can be e#tended easily to accommodatenew business units. /t is 3ust creating new data marts and then

integrating with other data marts.isad)antages of bottom-up design are9


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The positions of the data warehouse and the data marts arere)ersed in the bottom-up approach design.

ii! T"p-D"#n Desi$n%

/n the top-down design approach the, data warehouse is built$rst. The data marts are then created from the data warehouse.Ad)antages of top-down design are9Pro)ides consistent dimensional )iews of data across datamarts, as all data marts are loaded from the data warehouse.

This approach is robust against business changes. (reating anew data mart from the data warehouse is )ery easy.isad)antages of top-down design are9

This methodology is ine#ible to changing departmental needs

during implementation phase./t represents a )ery large pro3ect and the cost of implementingthe pro3ect is signi$cant.

Three-Tier Data Warehouse Ar/hite/ture2enerally a data warehouses adopts a three-tier architecture."ollowing are the three tiers of the data warehousearchitecture.

• :ottom Tier - The bottom tier of the architecture is the

data warehouse database ser)er. /t is the relational

database system. ;e use the back end tools and utilitiesto feed data into the bottom tier. These back end tools andutilities perform the +#tract, (lean, Load, and refreshfunctions.

• iddle Tier - /n the middle tier, we ha)e the OLAP *er)er

that can be implemented in either of the following ways.o :y


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( A//essii4it The OLAP tool should be capable of applying its ownlogical structure to access heterogeneous sources of dataand perform any con)ersions necessary to present a

coherent )iew to the user. The tool %and not the user&should be concerned with where the physical data comesfrom.

7( Consistent re0orting 0er8oran/ePerformance of the OLAP tool should not su0ersigni$cantly as the number of dimensions is increased.

9( C4ient:ser5er ar/hite/ture The ser)er component of OLAP tools should be su@cientlyintelligent that the )arious clients can be attached with

minimum e0ort. The ser)er should be capable of mappingand consolidating data between disparate databases.6( Generi/ Diensiona4it

+)ery data dimension should be equi)alent in its structureand operational capabilities.

;( Dnai/ s0arse atri< han.4ing The OLAP ser)er8s physical structure should ha)e optimalsparse matri# handling.

=( Mu4ti-user su00ort

OLAP tools must pro)ide concurrent retrie)al and updateaccess, integrity and security.


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&( 3nrestri/te. /ross-.iensiona4 o0erations(omputational facilities must allow calculation and datamanipulation across any number of data dimensions, and

must not restrict any relationship between data cells."%( Intuiti5e .ata ani0u4ation

ata manipulation inherent in the consolidation path, suchas drilling down or !ooming out, should be accomplished)ia direct action on the analytical model8s cells, and notrequire use of a menu or multiple trips across the userinterface.

""( !4e

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