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an OS/2 database product. In the mid-1990s, Novell also made a concentrated effort to make its NetWare operating system an attractive workgroup database server platform. From the earliest days of PC LANs, NetWare had become established as the dominant network operating system for file and print servers. Through deals with Oracle and others, Novell sought to extend this leadership to workgroup database servers as well. The arrival of Windows NT on the workgroup computing scene was the catalyst that caused the workgroup database market to really take off. While NetWare offered a clear performance advantage over NT as a workgroup file server, NT had a more robust, general-purpose architecture, more like the minicomputer operating systems. Microsoft successfully positioned NT as a more attractive platform for running workgroup applications (as an application server) and workgroup databases. Microsoft s own SQL Server product was marketed (and often bundled) with NT as a tightly integrated workgroup database platform. Corporate information systems departments were at first very cautious about using relatively new and unproven technology, but the NT/SQL Server combination allowed departments and non-IS executives to undertake smallerscale, workgroup-level projects on their own, without corporate IS help. This phenomenon, like the grass roots support for personal computers a decade earlier, fueled the early growth of the workgroup database segment. Today, SQL is well established as a workgroup database standard. Microsoft s SQL Server has been joined by Oracle, Informix, Sybase, DB2, and many other DBMS brands running on the Windows server platforms. Windows-based SQL databases are the second largest segment of the DBMS market and are the fastest growing. From this solid dominance in the workgroup segment, Windows-based server systems are mounting a continued assault on enterprise-class database applications, slowly but surely eating into low-end UNIX-based database deployments.

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For several years, the effort to make SQL a viable technology for OLTP applications shifted the focus away from the original relational database strengths of query processing and decision making. Performance benchmarks and competition among the major DBMS brands focused on simple transactions like adding a new order to the database or determining a customer s account balance. Because of the power of the relational database model, the databases that companies used to handle daily business operations could also be used to analyze the growing amounts of data that were being accumulated. A frequent theme of conferences and trade show speeches for IS managers was that a corporation s accumulated data (stored in SQL databases, of course) should be treated as a valuable asset and used to help improve the quality of business decision making. Although relational databases could, in theory, easily perform both OLTP and decision-making applications, there were some very significant practical problems. OLTP workloads consisted of many short database transactions, and the response time for users was very important. In contrast, decision-support queries could involve sequential scans of large database tables to answer questions like What is the average order size by sales region or How do inventory trends compare with the same time a year ago These

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3:

JDBC (Java Database Connectivity) is an API, modeled after ODBC, for connecting Java applications to a wide variety of relational DBMS products. Some JDBC drivers translate the JDBC API to corresponding ODBC calls and thus connect to the database via an ODBC data source. Other drivers translate directly to the proprietary client API of the particular relational database, such as the Oracle Call Interface (OCI). As with ODBC, an escape clause is available for passing proprietary SQL statements through the interface. The JDBC API offers the following features: Embedded SQL for Java The Java programmer codes SQL statements as string variables, the strings are passed to Java methods, and an embedded SQL processor translates the Java SQL to JDBC calls. Direct mapping of RDBMS tables to Java classes The results of SQL calls are automatically mapped to variables in Java classes. The Java programmer may then operate on the returned data as native Java objects.

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queries could take minutes or hours. If a business analyst tried to run one of these queries during a time when business transaction volumes reached their peak, it could cause serious degradation in OLTP performance. Another problem was that the data to answer useful questions about business trends was often spread across many different databases, typically involving different DBMS vendors and different computer platforms. The desire to take advantage of accumulated business data, and the practical performance problems it caused for OLTP applications, led to the concept of a data warehouse, shown in Figure 3-6. Business data is extracted from OLTP systems, reformatted and validated as necessary, and then placed into a separate database that is dedicated to decision-making queries (the warehouse ). The data extraction and transformation can be scheduled for off-hours batch processing. Ideally, only new or changed data can be extracted, minimizing the amount of data to be processed in the monthly, weekly, or daily warehouse refresh cycle. With this scheme, the timeconsuming business analysis queries use the data warehouse, not the OLTP database, as their source of data.

The original article can be downloaded from the Home Power website at: homepower.com/files/solarice.pdf

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