Hadoop: why enterprises need something to aspire to

Merv Adrian wrote a blog boast recently bemoaning the “aspirational marketing” that surrounds Hadoop, in particular the fact that current deployments are a long way from delivering on the vision.

While I completely agree that many enterprises are struggling to translate tactical use-cases into the business use-cases required to drive more strategic adoption beyond the proof of concept stage, I don’t think that aspirational marketing around Hadoop is necessarily a bad thing.

It is certainly true that part of the problem lies in clearly understanding how Hadoop can be used as a complement to traditional relational database technologies deployed as an enterprise data warehouse.

That is why we recently asked Is Hadoop a planet? – comparing confusion around Hadoop’s classification to that of Pluto – while also describing Hadoop as a framework in search of a metaphor.

Given the confusion, however, I believe it is incumbent on Hadoop providers to describe not just the functional use-cases that are driving tactical adoption, but also the bigger vision that will drive more strategic adoption.

The data management industry has become accustomed to thinking about the storage, processing and analysis of data in analytical databases as akin to warehousing, to the extent that the phrase ‘data warehouse’ no longer requires an explanation.

We believe that a good understanding of the potential strategic role of Hadoop, even if it is only aspirational at this stage, will be important in encouraging broader and deeper adoption of Hadoop.

In addition, it is not as if there are no enterprises deploying Hadoop more strategically. Cloudera estimates that about 20% of its 300 subscription customers are already deploying Hadoop as what it calls an Enterprise Data Hub.

I’m not personally convinced that Enterprise Data Hub is really the right term, (not least since we previously used the term Data Hub in a slightly different context). Other potential terms include data lake and data refinery.

Although the latter better describes Hadoop’s role in aggregating and processing data and the industrial-scale processes used to make data more acceptable for different analytic use-cases, it appears to have quickly passed out of fashion compared to the former.

I have begun using the term ‘data treatment plant’ as a combination of the two concepts to describe how Hadoop can be used as a single ‘logical’ unified data platform into which you simply poor data, while industrial-scale processes – the multiple data processing and analytic engines that will be supported by Hadoop 2.0: such as MapReduce, streaming processing, SQL and NoSQL – are used to make data more acceptable for a desired end-use.

451 clients can get more detail on the ‘data treatment plant’ and why we believe a bit of aspirational marketing may not be a bad thing for Hadoop, from our recent report, Hadoop: a framework in search of a metaphor.

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1 comment so far ↓

#1 Matt Brandwein on 01.29.14 at 2:06 pm

Matt, great post. As I mentioned on Twitter, at Cloudera we think “hub” is the right term vs. “lake” or “refinery”.

A lake is a passive repository, waiting for its data to be pumped out into other systems.
A refinery mainly transforms data (i.e. ETL) to feed other systems.
A hub does these things, and also does useful things on its own. With SQL, search, analytics, it’s now active.