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    <p><a href="http://en.wikipedia.org/wiki/NoSQL" rel="nofollow noreferrer">NoSQL</a> covers a diverse set of tools and services, including key-value-, document, graph and wide-column stores. They usually try improving scalability of the data store, usually by distributing data processing. Transactions require <a href="http://en.wikipedia.org/wiki/ACID" rel="nofollow noreferrer">ACID</a> properties of how DBs perform user operations. ACID restricts how scalability can be improved: most of the NoSQL tools relax consistency criteria of the operatioins to get fault-tolerance and availability for scaling, which makes implementing ACID transactions very hard. </p> <p>A commonly cited theoretical reasoning of distributed data stores is the <a href="http://en.wikipedia.org/wiki/CAP_theorem" rel="nofollow noreferrer">CAP theorem</a>: consistency, availability and partition tolerance cannot be achieved at the same time. SQL, NoSQL and NewSQL tools can be classified according to what they give up; a good figure might be found <a href="http://blog.nahurst.com/visual-guide-to-nosql-systems" rel="nofollow noreferrer">here</a>.</p> <p>A new, weaker set of requirements replacing ACID is <a href="http://en.wikipedia.org/wiki/Eventual_consistency" rel="nofollow noreferrer">BASE</a> ("basically avalilable, soft state, eventual consistency"). However, eventually consistent tools ("eventually all accesses to an item will return the last updated value") are hardly acceptable in transactional applications like banking. Here a good idea would be to use in-memory, column-oriented and distributed SQL/ACID databases, for example <a href="http://voltdb.com/" rel="nofollow noreferrer">VoltDB</a>; I suggest looking at these "NewSQL" solutions.</p>
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