data mart v data warehouse

8/12/2019 Data Mart v Data Warehouse

1/19

DATAMARTV DATAWAREHOUSEBy Elaine O Leary


2/19

DIFFERENTARCHITECTURALSTRUCTURES

A data mart and a data warehouse are essentially

different architectural structures, even though whenviewed from afar and superficially, they look to be

very similar.


3/19

WHATISADATAMART?

A data mart is a collection of subject areasorganized for decision support based on the needsof a given department. Finance has their data mart,

marketing has theirs, sales has theirs and so on.And the data mart for marketing only faintlyresembles anyone else's data mart.

The data mart is typically housed inmultidimensional technology which is great forflexibility of analysis but is not optimal for largeamounts of data. Data found in data marts is highlyindexed.


4/19

DATAMART

Data Mart Metadata

Extract

Transform

Load

Departmental

database

and relateddata from

other

Operational

Databases

DATA MART

Summarised Data

OLAP

Data Mining

EIS, APPS,

Reports

per functional area


5/19

WHATISADATAMART?

There are two kinds of data marts

Dependent and independent.

A dependent data mart is one whose source is a

data warehouse.

An independent data mart is one whose source isthe legacy applications environment


6/19

WHATISADATAWAREHOUSE?

Data warehouses are significantly different fromdata marts.

Data warehouses are arranged around the

corporate subject areas found in the corporate datamodel.

Usually the data warehouse is built and owned by

centrally coordinated organizations, such as theclassic IT organization.

The data warehouse represents a truly corporateeffort.


7/19

DATAWAREHOUSE


8/19

INTRODUCTION

Bill InmonsParadigm

Data warehouse is one part of the overall business

intelligence system. An enterprise has one datawarehouse, and data marts source their information

from the data warehouse. In the data warehouse,

information is stored in 3rd normal form


9/19

INTRODUCTION

Ralph Kimball's paradigm

Data warehouse is the conglomerate of all data

marts within the enterprise. Information is alwaysstored in the dimensional model


10/19

BILLINMON

Bill Inmon is recognized as the father of the data

warehouse and co-creator of the Corporate

Information Factory.

He has more than 35 years of experience in

database technology management and data

warehouse design.


11/19

RALPHKIMBALL

Ralph Kimball is known worldwide as an innovator,

writer, educator, speaker and consultant in the field

of data warehousing. He maintains a strong

conviction that data warehouses must be designed

to be understandable and fast

. He has written more than 100 articles and his

books on dimensional design techniques have been

the all-time best sellers in data warehousing.


12/19

DATAMARTSV DATAWAREHOUSE

The single most important issue facing the

information technology manager is whether to build

the data warehouse first or the data mart first.

The picture painted by the data mart advocates for

building the data warehouse is gloomy. It is also

self-serving and incorrect.


13/19

NEWAPPROACHES

In the early days of the data warehouse

marketplace, the data mart vendors tried to jump on

the warehouse concept by proclaiming that a data

warehouse was the same thing as a data mart.

The data mart vendors spread half truths and

misinformation about data warehousing.

The result form all this was only confusion

confusion.


14/19

NEWAPPROACHES

The customer discovered that when you don't build a

data warehouse, there is:

Massive redundancy of detailed and historical data from

one data mart to another,

Inconsistent and irreconcilable results from one data

mart to the next,

An unmanageable interface between the data marts and

The legacy application environment changes


15/19


Simply stated, for a variety of very powerful

reasons, you cannot build data marts, watch them

grow and magically turn them a data warehouse

when they reach a certain size. And by the same

token, integrating data across data marts is equally

unthinkable because each


16/19


The volume of data found in the data warehouse is

significantly different from the data found in the data

mart

Because of the volume of data found in the data

warehouse, the data warehouse is indexed very

lightly.

The technology housing the data warehouse is

optimized on handling an industrial strength amount

of data


17/19

DIFFERENCES.

The structure of the data in the data mart

(commonly a star join structure) is only faintly

compatible with the structure of the data in the

warehouse (a normalized structure).

The amount of historical data found in the data mart

is very different from the history of the data found in

the warehouse. Data warehouses contain robust

amounts of history. Data marts contain only modest

amounts of history.


18/19

DIFFERENCES

The subject areas found in the data mart are only

faintly related to the subject areas found in the data

warehouse.

The types of queries satisfied in the data mart are

quite different from those queries found in the data

warehouse.

The kind of users that are found in the marts are

quite different from the type of users that are found

in the data warehouse.


19/19

REALITY

There are simply MAJOR significant differences

between the data mart and the data warehouse

environment.

Just because they share basic characteristics at

some moment in time does not mean that a Data

Mart equals a DataWarehouse .

It is only a subset

data mart v data warehouse

Documents