NoSQL Databases for Big Data

Key-value stores can handle very large number of records. They can support high volumes of state changes per second with millions of simultaneous users through distributed processing and storage. Most of key-value databases hold their datasets in memory. That is why they are suitable for caching of intensive SQL queries. Furthermore, those stores enable to speed-up the display of web pages by calculating in advance parts of a webpage. The result can be retrieved and displayed quickly upon a request by user-ID.

They are very useful for both storing the results of analytical algorithms (such as phrase counts among massive numbers of documents) and for producing those results via reports.

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However, key-value databases inherit one drawback of NoSQL databases. They do not provide any kind of traditional database capabilities. Thus, to ensure transactions atomicity or the consistency of multiple parallel transactions, users should instead rely on the application itself.

Concerning flexibility, key-value stores enable to add at runtime any type of new values while preserving system availability. This is possible without compromising the already stored data that may have a different structure.

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