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In a distributed system, the code that performs business operations may be spread across multiple servers. A request will trigger the execution of code based on a server, and that code could possibly manipulate enterprise data. To prevent a threat to security, it is important that trusted requests be distinguished from those that are not. The server must verify the identity of the caller to evaluate whether the caller is permitted to execute the code. The client may also want to verify the identity of the server before engaging in the transaction for example, the consumer will not want to send a credit card number to www.stealyourcreditcard.com. A distributed system is typically made up of code executing on behalf of different principals (uniquely identified users or machines within the system). To obviate threats, the server requires that the caller provide credentials that are known only to the caller, as proof of identity. The credentials are then checked and verified with an authority, in what is known as the authentication process. Authenticated callers are then checked to determine whether they are permitted to access the requested resource; this is known as authorization. These are the fundamental phases in security threat prevention.

Obviously, it is impractical to believe that all threats can be eliminated (because new ones are developed every day). The objective is to reduce the exposure to a minimal and therefore acceptable level through using proper authentication and authorization augmented by the use of the security techniques including signing, encryption, and postfacto auditing. Java also provides some packages that can facilitate the security techniques used by an enterprise, as shown in Table 10-2.

A dimensional design for a relational database is called a star schema. Related dimensions are grouped as columns in dimension tables, and the facts are stored as columns in a fact table. The star schema gets its name from its appearance: when drawn with the fact table in the center, it looks like a star or asterisk. Figure 1-5 shows a simple star schema, based on the facts and dimensions for the orders process.

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program prompts you for objects to be scripted (see Figure 11-1 above).

TABLE 10-2

In a star schema, a dimension table contains columns representing dimensions. As discussed, these columns will provide context for facts. Sometimes, dimension table is simply shortened to dimension. This may give rise to confusion, since that word can also describe the individual columns. For example, when describing the schema shown in Figure 1-5, one might refer to the columns product, product_description, and SKU as dimensions, but it is also common practice to refer to the table itself as the product dimension. Generally, you can tell from the context whether the word dimension refers to a table or column. When it would not otherwise be clear, this book will use the terms dimension attribute and dimension table to make the distinction. The dimension tables serve to provide the rich context needed for the study of facts. In queries and reports, the dimensions will be used to specify how facts will be rolled up their level of aggregation. Dimension values may be used to filter reports. They will be used to provide context for each measurement, usually in the form of textual labels that precede facts on each row of a report. They may also be used to drive master-detail relationships, subtotaling, cross-tabulation, or sorts.

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