April 1, 2014
This report educates users about the many directions data warehouse (DW) architectures are evolving. Big data is a major driver of change with its burgeoning size, sources, frequency of delivery, and diversity of structures. In addition, the adoption of advanced analytics and real-time operation is equally influential on DW architectures. To assist users, many new products and technologies have arrived recently from software vendors and the open source community. This report describes all of the above and more.
“Architecture” means many things to many people, especially when the term is applied to IT systems. For example, many data warehouses are designed as multiple architectures that layer atop each other and work together, including a logical architecture (with data standards) and a physical plan. DW architectures also usually integrate with or overlap with related architectures for data integration, business intelligence, and enterprise data.
The modern data warehouse environment includes the usual marts, ODSs, and staging areas, as well as newer standalone platform types for DW appliances, columnar databases, NoSQL databases, Hadoop, real-time technologies, and various analytic tools. Given the rising complexity, data warehouse architecture is more critical than ever in order to make sense of, govern, and optimize the complicated multi-platform DW environments that many user organizations are building.
To help users prepare for new DW architectures, this report quantifies trends in data warehouse architectures and catalogs newly available, relevant technologies. The report also documents how successful organizations are evolving their architectures to leverage new business opportunities for big data. The goal is to provide data warehouse professionals and their business counterparts with the information they need before planning the next generation of their logical data warehouse architecture and its physical deployment.