Five Database Technology 'Faceoffs' Explained

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While technology has rapidly evolved over the past 40 years, the database systems that store and analyze our data are based off of algorithms that are decades old. SQL (Structure Query Language) is a special-purpose programming language meant for managing data held in relational database management systems (RDMBS). SQL was one of the first commercial languages created for RDMBS and has become the most used language, beginning in the late 1980s, through modern day. SQL works on a relational model, which separates data into interrelated tables of rows and columns. When data need to be retrieved from a relational database, they must be collected from multiple tables that are organized in a defined schema.

NoSQL, on the other hand, is a query language that became popular in the late 2000s, and is a model that aggregates data based on a defined key or value formula. NoSQL does not use a schema, making the data easier to retrieve. Just because NoSQL is different, however, does not necessarily mean it is better: since it does not use a schema, NoSQL cannot handle highly complex data sets as easily as SQL.

What more and more companies are realizing is that there is often a need for both technologies. Organizations are increasingly looking for solutions that can handle both relational and non-relational models.

Database technologies are rapidly evolving to a degree where it can be difficult to keep up with the newest solutions and buzzwords, let alone distinguish one from another. Looking for the right solution can be especially challenging when IT vernacular is constantly putting terms at odds. There are countless “this-or-that” conversations in database technology. In this slideshow, Kurt Dobbins, CEO of Deep Information Sciences, takes a look at a few of the most common faceoffs.  

 

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