Hadoop using MapReduce is a batch processing framework. Typically you churn through a lot of data in queries that take seconds, minutes or longer.
Hbase and Accumulo offer something more like a database modelled on the Google BigTable paper. These can service low-latency end-user facing queries. Accumulo has a number of particular extensions over HBase, in particular around much finer grained security labelling and the ability to efficiently run server-side functions.
Low latency is really what it's all about. In particular latency that is low enough that you could potentially use it to directly back applications servicing direct end users.
But if your interest here and in the other question re realtime is not just for low latency but true 'hard' realtime systems with all their consequent requirements then that's likely not a good fit for Hadoop or any of the related projects. Indeed when you take into account the basic mechanics of a distributed system adding hard realtime requirements would put you into a very specialised niche that most Hadoop use cases don't have to worry about.
Thanks Garry for clarifications ... It seems from all responses I got is that Hadoop may not the best options for hard real time processing, but at least it is capable of processing large base of data with an adequate speed.
Thanks again and have a nice day!
Best Regards, Mohamed El-Refaey
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