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Happiness Is… a Handhold on Hadoop
For a Hadoop solution do we look inside or outside?
Jun. 6, 2014 08:00 AM
As we know, the subject of Big Data and the ‘space race' to produce software application development functions that will enable us to extract insight and (therefore) value from the Big Data mountain remains one of the most discussed issues in information technology today.
Increasingly prevalent and popular, if not quite as ‘predominant' as some would have us believe, in this arena is Apache Hadoop. This software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
But there's a problem, because Hadoop is drastically underutilized in two respects:
What's the answer?
Do we look inside (@ logs) or outside (@ architecture)?
Looking inside at logs and logfiles - these are files that record "events" occurring throughout an operating system or software application or data management environment such as Apache Hadoop.
If we look at how our logs and logfiles are performing, then we can get information on hidden: errors, anomalies, problems and patterns... and these are the sorts of reports that can help guide DevOps (developer-operations) pros as they attempt to being a Hadoop project online.
The HP System Management Homepage (SMH) software function provides this kind of information to users working directly with the firm's own dedicated software for particular hardware. Elsewhere there are products such as XpoLog Augmented Search 5.0, which brings XpoLog's troubleshooting capabilities to the Hadoop platform. Put simply, it's a big expanding market.
... and then outside (@ architecture)?
This sponsored HP commentary has highlighted the firm's own product initially, but thankfully HP is big and bold enough not to shirk away from us being able to mention other vendors in this space (most of which will be key partners anyway) - so yes indeed competing products do exist from Cisco, Dell, IBM and others.
Ways to Improve the RDBMS with Hadoop
"Very low cost commodity hardware can be used to power Hadoop clusters since redundancy and fault resistance is built into the software instead of using expensive enterprise hardware or software alternatives with proprietary solutions. This makes adding more capacity (and therefore scale) easier to achieve and Hadoop is an affordable and very granular way to scale out instead of up. While there can be cost in converting existing applications to Hadoop, for new applications it should be a standard option in the software selection decision tree."
There is much to gain from intelligent implementation of Hadoop, but it's not easy and we need to look both inside and out (and back to front) in terms of where we can get guidance on best practice and efficiency in our implementation.
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