Architecture Dataware Housing An organization’s Enterprise Resource Planning systems

Architecture, in the context of an organization’s data warehousing efforts, is a conceptualization of how the data warehouse is built. There is no right or wrong architecture, but rather there are multiple architectures that exist to support various environments and situations. The worthiness of the architecture can be judged from how the conceptualization aids in the building, maintenance, and usage of the data warehouse.

One possible simple conceptualization of data warehouse architecture consists of the following interconnected layers:

Operational database layer

The source data for the data warehouse — An organization’s Enterprise Resource Planning systems fall into this layer.

Data access layer

The interface between the operational and informational access layer — Tools to extract, transform, load data into the warehouse fall into this layer.

Metadata layer

The data directory – This is usually more detailed than an operational system data directory. There are dictionaries for the entire warehouse and sometimes dictionaries for the data that can be accessed by a particular reporting and analysis tool.

Informational access layer

The data accessed for reporting and analyzing and the tools for reporting and analyzing data — Business intelligence tools fall into this layer. And the Inmon-Kimball differences about design methodology, discussed later in this article, have to do with this layer

Concept of Data Warehousing An organization’s Enterprise Resource Planning systems

Dataware Housing

The term Data Warehouse was coined by Bill Inmon in 1990, which he defined in the following way: “A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process”. He defined the terms in the sentence as follows:

Subject Oriented:

Data that gives information about a particular subject instead of about a company’s ongoing operations.

Integrated:

Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.

Time-variant:

All data in the data warehouse is identified with a particular time period.

Non-volatile

Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.

(Source: “What is a Data Warehouse?” W.H. Inmon, Prism, Volume 1, Number 1, 1995).

This definition remains reasonably accurate almost ten years later. However, a single-subject data warehouse is typically referred to as a data mart, while data warehouses are generally enterprise in scope. Also, data warehouses can be volatile. Due to the large amount of storage required for a data warehouse, (multi-terabyte data warehouses are not uncommon), only a certain number of periods of history are kept in the warehouse. For instance, if three years of data are decided on and loaded into the warehouse, every month the oldest month will be “rolled off” the database, and the newest month added.