“Conventional data management systems are fundamentally ill-suited for the world of data as it exists today. These systems, based with few exceptions on the relational data model, are broken because they integrate based on data location at the storage layer. While this approach worked reasonably well for the past 25 years, the world today has far too much data to use data location in storage as the basic lever.
The ill-suitedness of traditional, relational data model-based data integration tools reveals itself in several ways. The most obvious difficulties occur when combining several data silos or sources together because, in nearly all cases, they were modeled differently and conform to their own independent sets of rules and constraints. Data integration breaks down for two reasons. First, a single shared data model has to represent a global view over the sources. Second, significant manipulation and transformation are typically required to transform between the source and target schema as well as make the source data conform to a set of standardized rules.”
Read the full article by CEO Kendall Clark in DBTA’s Big Data Quarterly.