The document discusses the complexity of implementing a data warehouse. It involves multiple steps such as collecting business requirements, creating a data model and physical design, defining data sources, choosing database and reporting tools, and updating the warehouse. No single tool can handle all data warehouse access needs, so implementations rely on a suite of tools chosen based on the type of access required. Vendors have emerged that focus on fulfilling requirements like extraction, integration, and management of metadata for data warehousing. Solutions discussed include Prism Warehouse Manager, Carleton's PASSPORT, and SAS Institute products.

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.