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As with many large organizations, Bank of New Amsterdam (BNA) has several different operating systems running many kinds of software systems. The primary platform for the production business data is the IBM Mainframe S/390 (the mainframe). In addition, Microsoft NT boxes use Internet Information Services (IIS) and other Microsoft software to provide reporting capabilities, using a SQL Server shadow copy of the business data. BNA also has an investment in Oracle on the S/390. Several preexisting legacy applications provide trade-processing links to the major securities exchanges. These applications work well and have been developed and maintained for the past two decades, during which time a great deal of time and money has been spent. The applications do not need modification; instead, they need a new web front end to make them look good. The development environment is primarily Windows-based. The advantages of using the mainframe include its reliability, scalability, flexibility, and security. The mainframe has been running continuously for years in BNA without major problems. The mainframe can easily be amended to add hardware resources, such as CPU, memory, disk, or networking hardware, to increase capacity without changing the operating system or application systems. In addition, the CPU, memory, and disk space can be redistributed as application requirements change.

Traditionally, the downside to the mainframe has been the user interface the 3270 dumb terminal (the green screen), which is not user friendly. Prior to the commencement of BNAs Java project, another group spent a few months trying to develop Windows Active Server Pages (ASPs) to talk to the mainframe. The only solution providing ASP reports, a portfolio management system, solved the interface problem, but it was difficult to connect to the mainframe using ASP for trading. BNA has experienced difficulty getting the order messages to the mainframe. Along with performance problems, it seemed that every ASP order transaction required multiple dedicated connections to the SQL Server database. Fortunately, Java Database Connectivity (JDBC) connection pooling and Java s platform independence provided the performance and scalability that was needed. Moreover, it allowed us to take advantage of the mainframe for deployment and Windows for development. (Because budget is a limiting factor in an economic downturn, where every developer is competing for business, it is critical that you deliver a solution quickly that will integrate with an organization s existing infrastructure.) The IBM HTTP server, WebSphere application server, and Oracle 9i Enterprise relational database management system (RDBMS) are all in existence on the company s legacy IBM mainframe enterprise server. Table 11-1 describes the software components of the web front end. Figure 11-1 illustrates the enterprise architecture as a diagram, and Figure 11-2 shows a Unified Modeling Language (UML) sequence diagram.

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SELECT product, Sum(order_dollars) FROm month, product, order_facts_aggregate WHERE month.month='January' AND month.year = 2009 AND product.product_key = order_facts_aggregate.product_key AND month.month_key = order_facts_aggregate.month_key GROuP BY product

TABLE 11-1

Changes from the original query are highlighted in bold. No facts or dimension attributes have changed; only the names of tables and, in the case of month, the name of the surrogate key. TiP The facts and dimension attributes in an aggregate star conform to those in the base star. Rewriting a query to take advantage of the aggregate involves replacing table names and key columns.

Serves up JSPs and servlets, runs EJBs, and provides JDBC connection pools to user data Database required for application data (orderdb) ECMA JavaScript Web browser required to support JavaScript A library for parsing and generating XML documents

When aggregates are available, formulating a query requires choosing between the base star and the aggregate star. If there are multiple aggregates, there are still more options from which to choose. This may be an easy decision for a seasoned report developer to make, but it can be bewildering for a less experienced analyst or business user. Faced with a portfolio of aggregates, determination of the most appropriate schema for a query requires understanding what information is needed, identifying which stars can supply that information, and narrowing the pool down to the star with the smallest fact table. For example, Figure 15-2 illustrates a portfolio of aggregates that summarize the base order_facts star. A report is needed that shows sales by customer for the first quarter of 2009. Three stars can be used to answer this question: the base star, the month/salesperson/ customer aggregate, and the quarter/salesperson/customer aggregate. The latter is likely to have the least rows in its fact table and is, therefore, the best choice for this query. This selection process may be easy for developers who have been provided with basic schema documentation and some information about relative table sizes. For an end user, however, it may be difficult to choose. The situation is more complicated for ad hoc users, who may want to modify their query after seeing its results. Adding or removing dimensions from a query may change which star is able to answer most effectively, and may also alter which stars are able to answer at all. For example, the user may decide to take the Q1 2009 report of sales by product and break the

Oracle 10.0i Netscape/JavaScript Microsoft or Mozilla Firefox IBM/XML Parser for Java/3.1.1

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