Classic Data Warehouse Development

Classic Data Warehouse Development is the process of building an enterprise business model, creating a system data model, defining and designing a data warehouse architecture, constructing the physical database, and lastly, populating the warehouses database.

In a real business environment, the data warehouse is the main repository of the company’s historical data as well as data subscribed from other sources so that the company can up with statistical analysis to better understand the patterns and trends in the industry where they are operating. When they have a clear understanding of the industry trends, the can adjust their business rules and policies as well as come up with innovations in their products and services to gain competitive advantage over other companies within the same industry.

In the Classic Data Warehouse Development, the first step is to define the enterprise business model.

During this phase, all real life business activities are gathered and listed. A case model for the entire business is drawn. This includes interaction between the business and its external stakeholders. For a enterprise business model to be consistent, business requirements are identified using a very systematic and complex approach.

Some enterprise business modelers do base the functions on an organizational structure as it is prone to change over time with the fast changing of business trends and the potential growth. What is essential is a consistent framework for the business is defined that can last a long period.

An enterprise business models show how the business workers and other entities work together to realize business processes. The object model can be made from the aggregate collection of all the process and people and events involved.

When the enterprise business model is in place, the next step would be to create a system data model. This data model is actually an abstract data model describing how data is used. This data model represents entities, business events, transactions and other real life activities defined by the enterprise business model.

In a technical sense, the system data model would be used in the actual implementation of the database. The system data models are the technical counterparts of the entities created in the enterprise business model.

The next step would be defining the data warehouse architecture. The data warehouse architecture is a framework describing in details how the elements and services of the warehouse fit together and how to manage the data warehouse’s growth through time. Just like in building a house, there should be a set of plans, documents, specifications and blueprint.

When all is set, planned and documented, it will time to set up the physical database. The demand of the data warehouse specifies the need for a physical database system. Computer hardware is one of the biggest considerations in setting up a physical database.

The processing power of the computer should be able to handle labor intensive processing. The storage devices should be able to hold large bulks of data which get updated every few minutes. Networking support should be fast and efficient.

Other consideration in setting up the physical database is what software application to use and which vendor to buy from. There are plenty of relational database software applications available in the market. Some of these include Microsoft SQL, Sybase, PostgresSQL, Informix and MySQL.

When the physical database is set, measure and dimensions have already been laid. Measures are individual facts and dimensions refer to how facts need to be broken down.

For example, data warehouse for a grocery may have dimensions for customers, managers, branches and measures of revenue and costs. The next step in the classic data warehouse development is to populate the fact and dimension tables with appropriate data. The database may be set to it populated hourly, weekly or anytime depending on the need.

Editorial Team at Geekinterview is a team of HR and Career Advice members led by Chandra Vennapoosa.

Editorial Team – who has written posts on Online Learning.

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