The architecture consists of various
interconnected elements:
◦ Operational and external database layer – this
layer represents the source data for the DW. The
goal is to free the information locked up in the
operation databases.
◦ Information access layer – the tools the end user
access to extract and analyze the data. It
represents the tools that the end user normally
uses day to day to extract and analyze the data.
This layer consists of the hardware and software
involved in displaying and printing reports,
spreadsheets, graphs and charts for analysis.
Data access layer – the interface between the operational
and information access layers. A successful DW provides
end users with universal data access so that theoretically at
least end users should be able to access any or all of the
enterprise’s data necessary for them to do their job
regardless of location or information access tool.
Metadata layer – the data directory or repository of
metadata information. Meta data are data about the data
stored within the DW. Meta data keeps a record of
characteristics of the data, such as exactly what piece of
data exist, where they are located, where they came from
and how they can be accessed.
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Additional layers are:
◦ Process management layer – the scheduler or
job controller to keep DW up to date. Tasks
such as periodic download from identified
operational data stores, scheduled
summarization of operational data, access and
download of external data sources and update
the meta data are typically performed at this
layer of the DW.
◦ Application messaging layer – the “middleware”
that transports information around the firm.
This layer can also be used to collect
transactions or messages and deliver them to a
certain location at a certain time. In this sense,
the application messaging layer can be thought
as the transport system.
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Physical data warehouse layer – where the actual data
used in the DSS are located. In some cases, one can think
of the DW simply as a logical or virtual view of data,
because as we will see in coming chapters, in some
instances the data warehouse may not actually store the
data accessed through it.
Data staging layer – all of the processes necessary to
select, edit, summarize and load warehouse data from the
operational and external data bases. Data staging may
also require data quality analysis programs and other such
filters.
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Although the DW may appear to be the
source of data for various organizational
analysis initiatives and decision making
activities, it may not physically be the location
of the data being accessed. Numerous hybrid
mechanisms exist to structure of DW, but
three basic configuration can be identified;
virtual (point to point), central and distributed
data warehouses.
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The virtual data warehouse – the end users have direct
access to the data stores, using tools enabled at the data
access layer. Virtual data warehouses often provide a
relatively low-cost starting point for organizations to access
what types of data end users are really looking for.
The central data warehouse – a single physical database
contains all of the data for a specific functional area ,
department, division or enterprise. This warehouse approach
is often selection when users demonstrate a common need for
informational data and a large numbers of end users are
already connected to a central computer or network, usually
contains data from multiple operational applications.
The distributed data warehouse – the components are
distributed across several physical databases. Some
organizations push decision making down to lower levels of
the organizations, the data needed for decision making are
also pushed down to the LAN or local computer serving the
local decision makers. Many older DW implementations use
the distributed approach but with the advent of modern DW
implementation and management applications, reduced the
need for multiple or distributed DWs.
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The name suggests some high-level
technological concept, but it really is fairly
simple. Metadata is “data about data”.
With the emergence of the data warehouse
as a decision support structure, the
metadata are considered as much a
resource as the business data they
describe.
The metadata are essential ingredients in
the transformation of raw data into
knowledge. They are the “keys” that allow
us to handle the raw data.
For example, a line in a sales database may
contain: 1023 K596 111.21
This is mostly meaningless until we
consult the metadata (in the data directory)
that tells us it was store number 1023,
product K596 and sales of $111.21.
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The data warehouse is set up for the
benefit of business analysts and executives
across all functional areas.
In their individual databases, the different
areas may define and store data according
to their own version of the “truth”.
When data are retrieved from these
different areas and placed in the
warehouse, the transformation and
cleansing process ensures that there is a
single, integrated “truth” at the
organizational level.
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Regardless of the nature of a query,
certain aspects of the metadata are
important to all decision-makers.
Some of these are:
◦ What tables, attributes and keys
does the DW contain?
◦ Where did each set of data come
from?
◦ What transformations were applied
with cleansing?
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◦ How have the metadata changed
over time?
◦ How often do the data get
reloaded?
◦ Are there so many data elements
that you need to be careful what
you ask for?
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Transformation maps – records that show
what transformations were applied
Extraction history – records that show what
data was analyzed
Algorithms for summarization – methods
available for aggregating and summarizing
Data ownership – records that show origin
Access patterns – records that show what
data are accessed and how often
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THANK YOU
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