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The marketplace encapsulates the functionality of the multidimensional database in a variety of ways. Some implementations are positioned as a full-blown database management system, where a cube is managed on an MDB server. Other implementations utilize the cube to enable a specific front-end application; these tools may be categorized as OLAP products.
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Data Structure Relational Database Star Schema Multidimensional Database Cube
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Access Language Structured Query Language (SQL)
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Advantages Scalable Widely understood access language
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Interactive (OLAP)
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These distinctions are beginning to fade
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Figure 3-11 Alternative storage technologies for dimensional data
3 Stars and Cubes 55
Still other tools assemble cubes from the results of queries against a relational database. This enables them to offer limited slicing and dicing in the context of a business intelligence tool. These are sometimes referred to as ROLAP products. The distinctions between multidimensional and relational technology are beginning to fade. Many tools that we used to think of as relational database management systems (RDBMS) now incorporate multidimensional storage capabilities, often as a way to provide increased performance for relational data. These database products are now better described by the more general term database management system (DBMS). Additional innovations include SQL extensions for interaction with dimensional structures, the automated generation of cubes from tables, and the rewrite of queries from the SQL access language to MDX or vice versa. Vendors in these hybrid environments are also boosting the scalability of the cube, by allowing the database administrator to control the amount of precalculated intersections that are stored.
Cubes and the Data Warehouse
Each of the three data warehouse architectures from 2, Data Warehouse Architectures, can be adapted to incorporate the use of cubes. Used as a primary data store, the cube replaces a star schema to store dimensional data; as a derived data store, it supplements a star. When cubes are used as the primary storage for dimensional data, the solution may be limited by the scalability of the cube. A smaller number of dimensional attributes may be practical, and the grain of the cube may be limited. This may be a concern in a stand-alone data mart, or in a dimensional data warehouse architecture, where the dimensional data store is loaded directly from operational data. Any aggregation performed during this step represents analytic detail lost. The benefits may include the more expedient delivery of a solution and a high-performance analytic environment. This trade-off has lesser significance in a Corporate Information Factory, where a separate repository of atomic data is always maintained. Typically, the cube will replace relational storage as the primary data store in specific subject areas where the data sets are smaller. These subject areas are often supported by packaged solutions that provide a very tight analytic experience within this limited domain. A common example is budgeting, where vendors offer prebuilt analytics based on dimensional storage. Instead of displacing relational storage, the cube can also be used to supplement it. This practice allows the cube to participate in any of the architectures described in 2 as an additional layer. By supplementing relational storage with one or more cubes, developers can ensure comprehensive storage of detailed dimensional data in stars, while taking advantage of the performance and expressive capability of the cube. TIP Stars and cubes work well together. If you build a star, you can take advantage of the scalability of the relational database to store very granular and detailed information. From this detail, you can build cubes to support an interactive query experience. This coexistence of stars and cubes can take on two variations. In one configuration, a star schema may serve as the integrated repository of atomic data, while the cube serves as a data mart. In another configuration, the star schema may serve as a data warehouse
Part I
PART I Fundamentals
or mart, with cubes used as an additional layer for high-performance reporting. In either configuration, the cube serves the same purposes as derived schemas and aggregates, which are discussed in s 14 and 15, respectively.
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