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Definit= ions of a Data Warehouse
- W.H. Inmon
“A subject-oriented, integrated, time-variant and non-volatile collection o= f data in support of mana= gement's decision making process”
- Ralph Kimball
“A copy of transaction data, specifically structured for query and analysis”=
1.<= /b>
2.
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Data Warehouse
Customers
Etc…
Vendors
Etc…
Orders
Data
Warehouse
Enterprise
“Database”
Transactions
=
Copied,
organized
summarized
Data Mining
=
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Data Mart
oA Data Mart is a smaller, more focused Data Warehouse – a mini-warehouse.
oA Data Mart typically reflects the <= span style=3D'position:absolute;top:73.5%;left:12.35%;width:80.52%;height:7.0%'= >single subject area.
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Data Warehouse to Data Mart
<= ![if !vml]>
Data
Warehouse
Data Mart
Data Mart
Data Mart
= Decision
 Support Information
= Decision
 Support Information
= Decision
 Support Information
=
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Data Warehouse Operations
=
oRoll-up=
nAggrega= tes (summarizes) along a dimension
oDrill-d= own
nIncreas= es detail of a dimension
oSlice&#= 13;
nSelect a subset of the available dimensions
oDice= 3;
nGroup or partition on one or more dimensions
oPivot
<= span style=3D'font-family:Tahoma'>–Reorient a cube by swapping dimensions=
=
A numb= er of operations can be performed on the data warehouse quickly and efficiently= :

Roll-u= p, drill-down, slice, dice and pivot

Pivoti= ng through a “pivot table” have become so wide spread that they’re available in Excel.  Pivots = are more easily shown than they are described.
(http:= //www.cpearson.com/excel/pivots.htm)

What&#= 8217;s important to note here is not that these operations can be performed, but that they can be performed on TB’s of data in a reasonable amount of time.
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Data Cube
=
One wa= y that data may be stored in a data warehouse is called a “Data Cube”.   “Cube” is actu= ally a misnomer:  the data can be multidimensional – it is not limited to three dimensions.  It is not uncommon to have 10s o= r 100s of dimensions in a warehouse.  Storing the data in this fashion allows one to quickly determine useful information such as “How many people over 80 that make $120k= per year have defaulted on 15 year loans?”.

Althou= gh it is not obvious from this example, it is common for each dimension to be Megabyte= s of data and the entire warehouse to be Gigabytes or Terabytes of data.  Some companies have breached the Petabyte barrier.  Without t= he aforementioned advances and commoditization of storage and computing, aggregating and analyzing data at this scale would not be possible.

There = are other well understood warehouse storage techniques besides the data cube.  Some investigation will be requi= red to determine the best solution for a particular problem.
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OLAP Operations
A pict= orial view of the operations.

Some e= xamples for each:

Roll-u= p:  Applications by month, year, etc=
Drill-= down:  Just the opposite
Slice:=   Only those applications received= in 2000
Dice:<= span style=3D'mso-spacerun:yes'>  Middle aged applicants
Pivot:=   to facilitate visualization it m= ay help to swap two dimensions or to move from a three dimensional view to a= set of two dimensions views (it is typically easier to see the results of a p= ivot and understand what benefits it has rather than explaining it)

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Benefits of Data Warehousing
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oEnd users can ac= cess data quickly and easily= via Web browsers
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oEnd users can co= nduct extensive analysis with d= ata
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oEnd users have a consolidated view of organi= zational data.
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Data Life Cycle
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