EMERGENCE OF STANDARDS
Think back to our discussion in Chapter 1 of the dab' warehousing environment as bin of many technologies. A combination of multiple types of technologies is needed building a data warehouse. The range is wide: data modeling, data extraction data trim formation, database management systems, Control modules, and alert system agents, query 'tools, analysis tools, report writers, and so on.
Now in a hot industry such as data warehousing, there is no scarcity of vendors and products. In each of the multitude of technologies supporting the data warehouse, numerous vendors and products exist. The implication is that when you build your data wet house, many choices are available to you to create an effective solution with the best of breed products. That is the good news. However, the bad news is that have you (an multivalued products. the result could also be total confusion and chaos. These multivalued products have to cooperate and work together in your data warehouse.
Unfortunately, there are no established standards for the various products to exchange in and function together. When you use the database product from one vender the query and reporter tool from another vendor, and the OLAP (online analytical processing) product from yet another vender, these are three products have no standard meth for exchanging data. Standards are especially critical in two areas: metadata interchange and OLAP functions.
Metadata is like the total roadmap to the information contained in a data warehouse. Each product adds to the total metadata content; each product needs to use metadata created by the other products. Metadata is like the glue that holds all the functional pieces together.
No modern data warehouse is complete without OLAP functionality. Without OLAP, you cannot provide your users kill capability to perform multidimensional analysis, to view the information from many perspectives, and to execute complex calculations. OLAP is crucial…
In the following sections we will review the progress made so far in establishing standards in these two significant areas. Although progress has been made, as of mid-2000, we have not achieved fully adopted standards in either of the areas.
Think back to our discussion in Chapter 1 of the dab' warehousing environment as bin of many technologies. A combination of multiple types of technologies is needed building a data warehouse. The range is wide: data modeling, data extraction data trim formation, database management systems, Control modules, and alert system agents, query 'tools, analysis tools, report writers, and so on.
Now in a hot industry such as data warehousing, there is no scarcity of vendors and products. In each of the multitude of technologies supporting the data warehouse, numerous vendors and products exist. The implication is that when you build your data wet house, many choices are available to you to create an effective solution with the best of breed products. That is the good news. However, the bad news is that have you (an multivalued products. the result could also be total confusion and chaos. These multivalued products have to cooperate and work together in your data warehouse.
Unfortunately, there are no established standards for the various products to exchange in and function together. When you use the database product from one vender the query and reporter tool from another vendor, and the OLAP (online analytical processing) product from yet another vender, these are three products have no standard meth for exchanging data. Standards are especially critical in two areas: metadata interchange and OLAP functions.
Metadata is like the total roadmap to the information contained in a data warehouse. Each product adds to the total metadata content; each product needs to use metadata created by the other products. Metadata is like the glue that holds all the functional pieces together.
No modern data warehouse is complete without OLAP functionality. Without OLAP, you cannot provide your users kill capability to perform multidimensional analysis, to view the information from many perspectives, and to execute complex calculations. OLAP is crucial…
In the following sections we will review the progress made so far in establishing standards in these two significant areas. Although progress has been made, as of mid-2000, we have not achieved fully adopted standards in either of the areas.
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