Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 79

There is still no consensus on the phases that should be followed for
data warehouse design. Most of the topics in the data warehouse literature
follow a bottom-up, practitioner's approach to design based on the relational
model, using the star, snowflake, and constellation schemas, which we will
study in detail in Chap. 5 . In this topic, we follow a different, model-based
approach for data warehouse design, which follows the traditional phases for
designing operational databases described in Chap. 2 , that is, requirements
specification, conceptual design, logical design, and physical design. These
phases are shown in Fig. 3.6 . In Chap. 10 , which studies the design method
in detail, we will see that there are important differences between the design
phases for databases and data warehouses, arising from their different nature.
Also note that although, for simplicity, the phases in Fig. 3.6 are depicted
consecutively, actually there are multiple interactions between them. Finally,
we remark that the phases in Fig. 3.6 may be applied to define either the
schema of the overall schema of the organizational data warehouse or the
schemas of individual data marts.
A distinctive characteristic of the method presented in this topic is the
importance given to the requirements specification and conceptual design
phases. For these phases, we can follow two approaches, which we explain
in detail in Chap. 10 .Inthe analysis-driven approach , key users from
different organizational levels provide useful input about the analysis needs.
On the other hand, in the source-driven approach , the data warehouse
schema is obtained by analyzing the data source systems. In this approach,
normally, the participation of users is only required to confirm the correctness
of the data structures that are obtained from the source systems or to identify
some facts and measures as a starting point for the design of multidimensional
schemas. Finally, the analysis/source-driven approach is a combination
of the analysis- and source-driven approaches, aimed at matching the users'
analysis needs with the information that the source systems can provide. This
is why this approach is also called top-down/bottom-up analysis.
3.6 Business Intelligence Tools
Nowadays, the offer in business intelligence tools is quite large. The major
database providers, such as Microsoft, Oracle, IBM, and Teradata, have their
own suite of business intelligence tools. Other popular tools include SAP,
MicroStrategy, and Targit. In addition to the above commercial tools, there
are also open-source tools, of which Pentaho is the most popular one.
In this topic, we have chosen two representative suites of tools for
illustrating the topics presented: Microsoft's SQL Server tools and Pentaho
Business Analytics. In this section, we briefly describe these tools, while the
bibliographic notes section at the end of this chapter provides references to
other well-known business intelligence tools.