The use of Mashups to address enterprise needs has progressed in the adoption curve where the growth rate is becoming exponential. The technology is being leveraged with many industries to address unique business scenarios utilizing common “usage” and “architectural” patterns. More times than not, a solution for one industry can be deployed horizontally to cover other industries with similar needs. The differences in the mashup solutions are the roles of the users and the data sets being aggregated to create unique value.
In many cases the usage patterns (e.g. capturing of search criteria, rendering of data within the UI, updating of secondary tables based on primary data selection, manipulation of data, and communicating with others) are similar, but the business goals and objectives being addressed are distinctive for each customer. The design and architectural options of mashups are determined by the mashup platform chosen for implementation. Business scenarios that leverage a large set of common usage patterns can be implemented using a small set of design/architectural patterns (e.g. Use of RSS/ATOM feeds, invocation of REST services, widget communications via pub/sub model, aggregation of data sources through centralized hubs, data filtering at data/service sources).
This article will discuss the relationship of usage patterns and architectural patterns that can be used today for deploying enterprise mashups to address business needs. To see the article, open the PDF that is attached to this wiki page.