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Blogs > Bloor IM Blog

Acunu update
Philip Howard By: Philip Howard, Research Director - Data Management, Bloor Research
Published: 24th July 2013
Copyright Bloor Research © 2013
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Acunu has just released version 4.2 of Acunu Analytics. In case you don’t know, Acunu is a UK-based company that historically competed directly with DataStax as a purveyor of Cassandra-based solutions. However, while it used to maintain its own distribution of Cassandra - primarily because it didn't feel that the standard distribution was adequate - this is no longer the case now that Cassandra has been upgraded with contributions made by Acunu. Thus, although it continues to provide Cassandra support Acunu is now a BI/analytics vendor in this space rather than a big data database supplier. Which means that DataStax is now a potential partner for Acunu, rather than a competitor.

It is hardly surprising that Acunu should focus on its analytics product. Perhaps the most notable feature of this release are the extensions to Acunu’s version of SQL (AQL), in particular to support left and right outer joins. There is also the ability to embed mathematical functions such as square roots and logarithms within AQL statements. There are also a variety of performance (such multi-threaded Flume integration) and richer development capabilities (like drilling across from one dashboard to another), which you can check out on the Acunu website.

Going back to the issue of competition, Acunu's strength is in the real-time analysis of streaming data so its main competitors are CEP vendors. It can't offer the sort of scalability and performance you need in capital markets but that's a specialised area and, actually, most query environments do not have those sorts of requirements. The big advantage that Acunu has, of course, is that the solution is much less expensive: CEP engines typically run on standard hardware as opposed to a big data cluster and that can save you a bushel of money. Indeed, there are big data applications that are cost-justifiable because of this big data approach that would simply not be feasible otherwise.

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