The connection between data in Hadoop and advanced analytics

It is undeniable that a massive amount of data (read:
multi-structured data) can be stored in Apache Hadoop. However, when it comes
to unlocking so much data, business analysts are often seen looking for easy
ways to do the needful. Perhaps without any relevant programing skills, they
find it difficult to analyze the data and transform it into business insights.
Not to mention, at times, even the lack of distributed processing skills can act
as an obstacle when they are looking forward to have their way with advanced analytics. Nevertheless, in
either of these situations, what is required is a solution that can come in
handy when the business analysts try to access the data in Hadoop in a more
direct manner.

Interestingly, there are quite a few solutions that can
serve the purpose and help the analysts in deriving business insights. However,
in order to identify the right one, they may want to crosscheck if all or at
least some of the following requirements are being duly met:

However, the question remains that why exactly is such a
solution required in the first place? As already mentioned, the Hadoop
MapReduce jobs can be quite complex to deal with (read: develop). Here, it is
worth mentioning that these jobs play an important role when it comes to
processing the data that is stored in the Hadoop
Distributed File System (HDFS). And obviously, until this data is processed
in batch modeArticle Search, it can be difficult to go any further with
advanced analytics.

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