As my long-time readers have certainly noticed, the frequency of my posts have lengthened over the past few months. First, I would like to offer my apologies for being remiss in that department but I have had some extenuating circumstances.

For over three years I have been closely observing and actively participating in the government’s transition to cloud computing. Many of my observations, experiences and insights have been revealed through “Cloud Musings”, “Cloud Musings on Forbes” and in my first book “GovCloud: Cloud Computing for the Business of Government”. In parallel, I have also been designing strategies, building processes and creating transition tools all designed to reduce the risk and increase the value of cloud transformation projects. The culmination of my personal efforts and those of the entire team here at NJVC have now all been folded into our new brand for cloud computing operational excellence – Cloudcuity™

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Telx on Wednesday announced the availability of Amazon Web Services Direct Connect offerings for Telx customers in Northern California, as well as providing extended availability to Telx Cloud Connection Centers in adjacent markets.
“Telx continues to expand the portfolio of options available to its clients to build next-generation distributed infrastructures. Rather than just a single solution, clients can leverage a range of public and private network transport and interconnection options to deploy optimal hybrid architectures, across a range of multilayer network services, utilizing a mix of colocation, private, hosted private, and public cloud services,” said Joe Weinman, senior vice president of cloud services and strategy for Telx. “We are excited to offer our clients direct access to Amazon Web Services, a leading provider and innovator in the cloud.”

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NoSQL and Hadoop environments are a new challenge for performance engineers. NoSQL solutions require more logic to the application and rely on accurate access patterns to perform. Ensuring MapReduce performance and scalability is a Big Data problem in itself.
In his session at the 11th International Cloud Expo, Michael Kopp, a technology strategist in the Compuware APM, will show how to tackle both challenges with the help of modern Application Performance Management.
Michael Kopp is a technology strategist in the Compuware APM center of excellence and has more than 10 years of experience as an architect and developer in the Java/JEE space. Additionally, Kopp specializes in architecture and performance of large-scale production deployments with a special focus on virtualization and cloud.

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‘Big Data’ has a problem, and that problem is its name.
Dig deep into the big data ecosystem, or spend any time at all talking with its practitioners, and you should quickly start hitting the Vs. Initially Volume, Velocity and Variety, the Vs rapidly bred like rabbits. Now we have a plethora of new V-words, including Value, Veracity, and more. Every new presentation on big data, it seems, feels obligated to add a V to the pile.
But by latching onto the ‘big’ part of the name, and reinforcing that with the ‘volume’ V, we become distracted and run the risk of rapidly missing the point. The implication from a whole industry is that size matters. Bigger is better. If you don’t collect everything, you’re woefully out of touch. And if you’re not counting in petas, exas, zettas or yottas, how on earth do you live with the shame?

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One of the phrases you hear associated with cloud computing is “architecting for failure.” Rather than build in a lot of hardware-level redundancy – power, disk, network, etc… – the idea is that you expect it to fail and can simply replace the application (which is what you care about anyway, right?) with a clone running on the same cheap hardware somewhere else in the data center.
Awesome idea, right?
But when it comes down to it, cloud computing environments are architected for scale, not fail.

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By Tony Baer, Principal Analyst, Enterprise Solutions, Ovum

Splunk, which specializes in delivering a data platform for “machine data,” is approaching a turning point. The explosion of sensory data – part of the Big Data phenomenon – is pulling the company in different directions. With a base as the data platform for IT systems management and security programs, Splunk could expand to other forms of machine data such as smart public infrastructure.

Or, as implied by the recruitment of key product executives from SAP and Oracle, it could venture higher up the value chain, developing more business-focused solutions around this competency. Either way, Splunk must choose its targets carefully. As a $150–$200m company, it can’t be all things. Splunk is already promoting itself as an operational intelligence platform that provides quick visibility of trends from low-level data. However, Ovum believes that the company could get more mileage in the market …

Tibco Software is holding its annual user conference this week in Las Vegas, and Big Data is among the big topics of discussion. The company claims it was doing big data before there was Big Data, which reminds me of all the companies who’ve said they’ve been doing cloud computing for, you know, ever. (Disclosure: I worked at Tibco from 2006-09, a time during which it acquired Spotfire, which did bring a layer of analytics and big data-ish capability to the company.)

In any case, Big Data has been dominating my twitter timelines the last few weeks, with a sharp spike the likes of which I’ve not seen before.

The Big Data landscape looks like utter chaos to me right now. I don’t know how to define Big Data, and not even NIST has defined it, the way it has helpfully if loosely defined cloud computing.

NIST did hold a workshop on the topic back in June. The first thing to jump out at me from that event was the old-line Big Data people – aka scientists – gamely outlining the classic Big Data applications – nuclear-event simulation, epidemiology, metereology, and other massively computational uses, while noting that NIST had its first seminar on this topic in 1997.

But today, Big Data has been hijacked by real-time data-collection activities, and all the nefarious uses to which it can be put by marketers. There’s still big science involved in developing Big Data frameworks and apps, but the application focus is moving from controlling measles and predicting hurricanes to capturing eyeballs and selling coupons.

In addressing issues such as sensor density, sampling rates, and real-time graphic simulation, I’m reminded of the late Argentinian author Jorge Borge’s perfect map of the world – one which would be the same size as the world and feature every detail precisely.

In other words, by creating perfect pictures of the world (or perfect pictures of a customer’s buying activity), are we missing something essential in the human mind’s ability for abstraction? Would Big Data helped design the original Ford Mustang and identify its buyers? Did it drive the vision behind the iPod, iPhone, and iPad?

So yes, I’m uncomfortable with the all-out onslaught on the term Big Data. I fear its best uses may be obscured and perhaps lost within a miasma of marketing-driven bloviation. The same thing almost happened to cloud computing, and with today’s conflation of Big Data and cloud computing, it could still happen.

I’ll be at Cloud Expo/Big Data Expo in November, wearing my hairshirt and admonishing anyone who’s abusing the terms. I may not have such a problem there, though, as I expect discussions to be highly technical and useful. Maybe I just need to stop taking my twitter timeline so seriously.

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