Should all companies start to invest in NoSQL databases?

By: Bart Baesens, Seppe vanden Broucke

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You asked: Should all companies start to invest in NoSQL databases?

Our answer:

The explosion of popularity of NoSQL databases should be put in perspective considering their limitations.  Most NoSQL implementations have yet to prove their true worth in the field (most are very young and in development).  Most implementations sacrifice ACID (atomicity, consistency, isolation and durability) concerns in favor of being eventually consistent, and the lack of relational support makes expressing some queries or aggregations particularly difficult, with map-reduce interfaces being offered as a possible, but harder to learn and use, alternative.  Combined with the fact that RDBMSs do provide strong support for transactionality, durability and manageability, quite a few early adopters of NoSQL were confronted with some sour lessons.  It would be an over-simplification to reduce the choice between RDBMSs and NoSQL databases to a choice between consistency and integrity on the one hand, and scalability and flexibility on the other.  The market of NoSQL systems is far too diverse for that.  Still, this tradeoff will often come into play when deciding on taking the NoSQL route.  We see many NoSQL vendors focusing again on robustness and durability.  We also observe traditional RDBMS vendors implementing features that let you build schema-free, scalable data stores inside a traditional RDBMS, capable to store nested, semi-structured documents, as this seems to remain the true selling point of most NoSQL databases, especially those in the document store category.  Expect the future trend to continue towards adoption of such “blended systems”, except for use cases that require specialized, niche database management systems.  In these settings, the NoSQL movement has rightly taught users that the one size fits all mentality of relational systems is no longer applicable and should be replaced by finding the right tool for the job.  For instance, graph databases arise as being “hyper-relational” databases, which makes relations first class citizens next to records themselves rather than doing away with them altogether.