Bridging Relational and NoSQL Worlds

Bridging Relational and NoSQL Worlds

Copyright: © 2018 |Pages: 62
DOI: 10.4018/978-1-5225-3385-6.ch005
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Abstract

The chapter discusses the fact that the development and use of NoSQL databases showed that neither everything was good in NoSQL nor everything was so bad in relational databases. Namely, when operating with data, NoSQL databases have identical requirements for entering, updating, deleting or searching data, or for the data manipulation that SQL already resolved long ago. Therefore, it is not surprising that further development of many NoSQL databases shifted towards supporting SQL, which is one of the topics of this chapter. Namely, database users are generally not concerned with details about how data is stored. Rather, they want to have the possibility to view and analyze data together, regardless of whether the data is stored in relational or NoSQL databases. Therefore, vendors of relational databases were forced to look for solutions that would allow them to work with data stored in NoSQL databases as well.
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From Nosql Toward Sql

Development of databases showed that NoSQL moved away from relational databases because of the structural relational constraints it imposed, especially those related to the ACID transactions, which became a serious limitation to scaling and dealing with large data sets. Although the umbrella name for the new generation of databases is NoSQL, the recent development of those databases is showing that they are not necessarily opposed to SQL (i.e., that NoSQL means not just SQL). Today it is generally accepted that the aim of NoSQL databases is not to abandon SQL, but to find solutions to overcome technical limitations of the relational databases, especially when it comes to working with Big Data. It seems that NoSQL should really have been NonRel, implying nonrelational (Tiwari, 2011). Namely, every day more and more NoSQL databases provide support for SQL or create query languages in a syntax very similar to SQL. One of the reasons for the shift to SQL probably lies in the fact that, when it comes to data manipulation and search, NoSQL databases have requirements almost identical to those of relational databases. Namely, both the databases must allow entering, updating, deleting, and searching for data, except that all this was already solved long ago in SQL—the query language that was mainly associated with relational databases until now. After the initial enthusiasm regarding various NoSQL programming languages and APIs, SQL turned out to have significant advantages. It is a language with a high-level abstraction that simplifies access to and manipulation of data. In addition, it is a language in which literally millions of database users are conversant, and there are hundreds of popular business intelligence and analytic tools that use it under the hood as the means for getting at data (Harrison, 2015).

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