Abstract
The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.
Highlights
Most large enterprises seem to be taking care of minimizing the application maintenance of existing production systems
The authors of Ref. 1 built on discussions with nearly 20 database administrators (DBAs) at three very large enterprises:
A strong motivation for this approach is the fact that when designing a database for Big Analytics, we must consider data mining (DM)/machine learning (ML) patterns, clustering of some attributes, etc. to ensure adequate system performance
Summary
Most large enterprises seem to be taking care of minimizing the application maintenance of existing production systems. The initial enthusiasm for XML databases was based on Web application architectures and service orientations that use XML as a means to standardize data exchange format. This is already possible with document-oriented NoSQL databases (see the popular JSONa format), though not in such powerful languages as the XQuery in XML environment. Analytical processing of large data volumes requires new database architectures and new methods for data analysis. This work is an extended version of the conference paper.[3] In Sec. 2, we brie°y describe the Big Analytics concept, i.e. the properties of processing and analysis of large volumes of data.
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