Red Hat has been doing what it's doing for quite a while now, and so far, it seems to be working out pretty well. Every once in a while, though, along comes a little independent validation about the viability of open source in the business world that deserves to be called out.

The most recent example is from The New York Times, specifically, an article that (rightly) highlights the need for data analytics and shepherding in the big data arena. As you might have guessed, I am personally in agreement with this point, because for a long time in big data there's been a whole lot of data gathering and not a lot of data analysis. What analysis there has been has been rife with a great deal of inane conclusions ("consumers love Product X!") and the occasional cool data analysis that makes you actually think about the world around you.

Given that it's human beings that are managing and looking at this data, that's to be expected, I suppose. We live in a world of mediocrity punctuated by flashes of brilliance. It's these flashes that tend to be remembered and have a legacy, and in this same article, there's an undercurrent that highlights – in a matter-of-fact tone – the brilliance of open source.

"Take Cask, a start-up in Silicon Valley founded in 2011, backed by leading venture capitalists and led by former Facebook and Yahoo engineers. In late September, the promising young company changed both its name and its business model — moving to supplying open-source software and trying to make money on technical support and consulting rather than on proprietary products."

This one paragraph is what made the point so well: just a nonchalant outline of what Cask, a Hadoop applications development company, has had to do to be nimble in the Boomtown atmosphere of Big Data right now. No explanations beyond this paragraph of what open source is or why it is the spawn of demons/greatest thing since sliced bread. Just the facts, and how Cask is faring with their call to change course.

The article is not about Cask, mind you; it's about how companies can generate revenue around big data. But the mentions of other companies who rely on open source software in their business models, such as Hortonworks and Cloudera, only serves to make the point. Organizations who are building on open source technologies are the ones who are making money in this space, which is a far cry from the traditional proprietary "lock it down" path many start ups have tried in the past.

In the course of rounding up successful and potentially successful companies in big data, Steve Lohr's piece has implicitly highlighted the benefits of open source at the same time. And it's the lack of fanfare that makes the point that much stronger.