Implementation consideration

1. S.Subha Thilagam.
M.sc(cs)

2.  A data warehouse cannot be simplified bought and
installed its implementation requires the integration
of many products within a data warehouse.
 The caveat here is that the necessary customization
drives up the cost of implementing a data
warehouse.

3. To illustrate the complexity of the data warehouse
implementation,
 Collect and analyze business requirements.
 Create a data model and a physical design for the data
warehouse.
 Define data sources.
 Choose the database technology and platform for the
warehouse.
 Choose the database access and reporting tools.
 Choose database connectivity software
 Update the data warehouse.

4.  Currently no signal tool on the market can handle all
possible data warehouse access needs.
 Most implements rely on a suite of tools.
 The best way to choose this suite includes the
definitions of different types of access to the data and
selecting the best tools for this kind of tools.

5.  Most of these tools are designed to easily compose
and execute ad hoc queries and build customized
reports with little knowledge of the underlying
database technology.
 OLAP and Data mining tools are used .
 Business requirements that exceed the capabilities of
ad hoc query and reporting tools are fulfilled by
different classes of tools.

6.  Simple tabular form reporting.
 Ranking.
 Multivariable analysis.
 Time series analysis.
 Complex textual search.
 Statistical analysis.
 Predefined repeated queries.
 Interactive drilldown reporting and analysis.

7.  The ability to identify data in the data source
environment that can be ready by the conversion tool
is important.
 Support for flat files , indexed files is critical ,since
the bulk of corporate data is still maintain.
E . g., virtual storage access method and egacy DBMS.
 The specification on interface to interface the data to
be extracted criteria is important.

8.  The ability to read information from the data
dictionaries or import information from repository
products is desired.
 The code generated by the tool should be completely
maintainable from within the development
environment.
 Selective data extract of both data elements and
records enables users to extract only the required
data.
 Vendor stability and support for the product are items
that must be carefully evaluated.

9. Vendor solutions:
Some vendors have emerged that are more focused
on fulfilling requirements pertaining to data
warehouse implements as opposed to simply moving
data between hardware platforms.
 Prism markets a primarily model-based approaches
on the ware housing extraction function, while
builders markets a gateway approach.
 SAS products could handle all the warehouse
functions, including extraction.

10. Prism solution:
 Prism warehouse manager maps source data to a
target database management system to be used as a
warehouse.
 The warehouse manager extract and integrate data,
create and manage metadata, and build a subject-
oriented, historical base.
 Prism solutions has relationship with Pyramid and
Informix.

11. Carleton’s PASSPORT:
 PASSPORT is positioned in the data extract and
transformation of data warehousing.
 The product currently consists of two components.
 The first, is collects the file-record d- table layouts
for the inputs and outputs and converts them to a
passport data language.
 The workstation based and is used to create the
metadata directory from which it builds COBOL
programs to create the extracts.

12. SAS institute:
 SAS begins with the premise that most mission-
critical data still resides in the data center and
offers its traditional SAS system tools .
 This data repository function can act to build the
information database.
 SAS engines can work with hierarchical and
relation database and sequential files.
 SAS is act as a front end in SAS reporting and
graphing products.