Introducing Data Science: Big data, machine learning, and more, using Python tools by Davy Cielen Arno D. B. Meysman Mohamed Ali

Author:Davy Cielen, Arno D. B. Meysman, Mohamed Ali
Language: eng
Format: epub
Publisher: Manning Publications

Two more database types emerged in the quest for partitioning: the document store and the graph database.

We’ll go into detail on each of the four types later in the chapter.

Please note that, although size was an important factor, these databases didn’t originate solely from the need to handle larger volumes of data. Every V of big data has influence (volume, variety, velocity, and sometimes veracity). Graph databases, for instance, can handle network data. Graph database enthusiasts even claim that everything can be seen as a network. For example, how do you prepare dinner? With ingredients. These ingredients are brought together to form the dish and can be used along with other ingredients to form other dishes. Seen from this point of a view, ingredients and recipes are part of a network. But recipes and ingredients could also be stored in your relational database or a document store; it’s all how you look at the problem. Herein lies the strength of NoSQL: the ability to look at a problem from a different angle, shaping the data structure to the use case. As a data scientist, your job is to find the best answer to any problem. Although sometimes this is still easier to attain using RDBMS, often a particular NoSQL database offers a better approach.

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