Architecting the data layer for analytic applications
At a glance
Strong analytical capabilities are at the core of todays business. Analytics are a major component of any Business Intelligence (BI) application, but traditional data strategies no longer work in todays data-intensive, high-volume, read-only environment for large scale data-analysis (Big Data) and very large data warehouses.
The large volumes of both structured and unstructured data currently bombarding global organizations are intensifying their need for stronger analytical capabilities. Analytics are a major component of any Business Intelligence (BI) application, but traditional data strategies no longer work in today’s data-intensive, high-volume, read-only environment for large-scale data analysis (BigData) and very large data warehouses.
As a result, data architects are struggling to tailor a data processing and storage strategy that not only fits their organization’s unique analytic needs and circumstances, but is also aligned with managements’ overall business goals. While the emergence of non-traditional niche architectures has expanded the scope of possibilities from which to select, it has also added more complexity to the mix. Today’s challenging environment requires efficiency in query throughput and bulk data loading with a reasonable latency acceptance. The mingling of structured and unstructured data, along with the advent of Semantic Web technologies, require experimentation with some of these emerging technologies as data processing and storage models.