Design of the Relational Database and OLAP Cubes and Data mining tools The next phase in the data warehouse design is the design of the relational database and the OLAP cubes. With this the design phase moves into a more granular level of designing surrogate keys, primary and foreign key relationships, Views, indexes, partitions of fact tables. OLAP cubes are created to support the query needs of end users.

Dimension tables are indexed on their primary key. These are generally surrogate keys created in the data warehouse tables. The fact table must have a unique index on the primary key. Indexes on primary keys can be clustered or non clustered depending on the volume of schemas being maintained in the data warehouse.

Views are created for users who need direct access to data in the data warehouse relational database. Indexed views are used to improve performance of user queries that access data through views. View definitions create column and table names that make sense to the end user.

The applications that support data analysis are constructed in this phase of the design. OLAP cubes design requirements are then defined by the parameters set in the dimensional model of the data warehouse. Cubes provide fast access to data in a data warehouse. A cube has been defined as a multidimensional data structure with defined dimensions and measures and constructed out of a subset of data of the data warehouse.

Though data mining does not determine the wrap and hoof of the data warehouse design, data mining is the final manifestation of the data warehouse. Data mining uses sophisticated algorithms to analyze data and create models that represent information about the data. Data mining models are used to predict characteristics of new data or to identify groups of data entities.

Metadata:

The data warehouse reflects the business model of the enterprise and therefore an essential element of data warehousing design is the creation and management of metadata. Metadata is data about data. Many different kinds of metadata are to be created and managed. Administrative metadata includes the information necessary for setting up and using the warehouse. Business metadata includes business terms and definitions, ownership details and changing policy details. Operational metadata stores information that is collected during the operation of the warehouse such as the lineage of migrated and transformed data, the currency of data etc.

Security considerations and backup and recovery plans

It is important to build in adequate security measures into the data warehouse system. The organizations security policy will impact on who can access or view data. Security policies will demand the creation of multiple copies of the data or creation of data views and possible classification of data on basis of security codes.

Closely allied to the security considerations is the backup and disaster recovery plans for the data warehouse.

The key to successful data warehousing is data design as defined by end users. Business users know what they want and they focus upon it. They will determine what data will be needed, where to locate it, how it can harnessed, what dimensions are to be created and what output is expected. The remaining tasks are a natural outcome of the business needs. Therefore, the best data warehouse design naturally and gracefully flows out of the business requirements of the end user.