A parallel computer paired with a parallel database engine increasingly is touted by vendors, analysts and users as the preferred solution for the rapid access, flexible queries, modeling and analysis needed in decision support systems. It finds many ready uses. A stock trader who can analyze derivatives faster than competitors makes more money. Large retailers can study the buying trends of groups or individuals. Banks can detect credit card fraud.
Airlines can analyze travel patterns.
Pharmaceutical companies can tailor sales pitches to individual doctors based on their prescription writing patterns. Because decision support applications like these are mission-critical, parallel processing earns consideration. Before getting into the technical details, an organization should identify its database and data query needs. Once IS staff and business managers understand the data they have or will have, as well as their business processes, what access they need and how much they are willing and able to spend, they can decide whether parallel processing may be the answer. If that decision is affirmative, they are ready to choose a database engine and a hardware architecture.