Modeling and Management of Big Data in Databases

1.3. Data Abstraction Levels

Generally, in the design of both relational and NoSQL databases, three levels of abstraction are used: conceptual, logical and physical. Data modeling is understood as the technique that records the features of data elements in a map that describes the data used in a process. Data modeling illustrates how the data elements are organized and related. Relational modeling methodologies have well established procedures, as a result of decades of research; however, for NoSQL databases the modeling methodologies, specifically for Big Data, are a novel topic that continues to be studied.

Data modeling at the conceptual level is closely related to the scope of the business process. Therefore, the conceptual model is technologic-agnostic and independent of the database to be used. Thus, already-known models for relational databases can be used in non-relational databases. At the logical level, the modeling is focused on the data model to be used. For NoSQL databases, the modeling is aimed at representing the data structure of the column-oriented, document-oriented, graph or key-value models, as described previously. On the physical level, the modeling will represent the own schema of the selected database; that is, the specific implementation of the NoSQL database, such as Cassandra or MongoDB.