Mashup Patterns: Design Patterns for Real-time Data Analysis

Historically, data integration has centered around a data warehouse, with data ingest processes and algorithms that normalize data models, and queries that access this integrated data model.

Mashups, which are largely characterized by a lack of the data warehouse, must address the same concerns, matching of models from disparate data sources and careful access of the integrated set, however, the patterns are markedly different. Often, mashups are ad-hoc and may be used only once, however, the programming style is favorable even for solutions that will be in use for an extended period. In this alternate paradigm, where analysis is performed directly against the original data sources, interesting patterns emerge.

In this session we give an overview of the differences in style between traditional data integration and mashup approaches and we will study patterns such as resource composition, iteration, caching, filtering and error handling.

Cornelia Davis is a Senior Consultant Technologist at the Architecture and Applied Research Group in the Strategy Office of EMC, focusing on RESTful Service Oriented Architectures. A self-proclaimed propeller head, her areas of expertise include XML, Atom, mashups and programming models, and she is a frequent speaker on RESTful SOA. As an expert in this architectural style of the cloud, she regularly consults with product groups across the company to ensure that the latest advances in web architectures are appropriately applied in EMC products. Cornelia holds a B.S. and an M.S. in Computer Science from California State University, Northridge, and completed additional graduate work focusing on programming languages and theoretical computer science at Indiana University.