You have heard how important avoiding vendor lock-in has become when thinking about hosting your applications in the cloud.
In his session at the 11th International Cloud Expo, Steven Citron-Pousty, Developer Advocate and OpenShift Product Marketing Manager at Red Hat, will show you how easy avoiding vendor lock-in can be. Using a Java application running locally on a traditional laptop dev environment, he’ll move the development and runtime of the application to a public PaaS, and then to a private PaaS. All without modifying application code or the development tools we started with.

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Open source software stacks offer a huge amount of customization so the cloud being built does what is expected – and without huge licensing fees.
A recent article on GigaOm.com asks, “How can open-source cloud management tools like Eucalyptus, CloudStack and OpenStack impact the adoption of cloud technology?”
Lowering the barrier to entry
Most organizations adopt cloud to optimize their IT investment, to improve existing services or to support new business and service models, according to the article. In this scenario, open-source lowers the barriers for new organizations to build their private cloud.
Facilitating custom clouds
Many organizations appreciate that open source allows great customization to meet individual requirements. They can build a differentiated cloud service to meet customers needs.
Open-source clouds beget more clouds
Open source also encourages and supports innovation in the development of new cloud products.
User-supplier collaboration makes a better cloud
Collaboration between technology providers and users is a huge advantage in the open source arena. In open source projects, the project roadmap is driven by user needs. That means features will meet real end-user expectations.

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Workday, the cloud-based HR start-up co-founded by ex-PeopleSoft executives David Duffield and Aneel Bhusri, who vamoosed after Oracle acquired PeopleSoft in a hostile takeover in 2005, is set to go public next Friday.
It means to sell 22.75 million Class A shares for between $21 and $24 each. Using the mid-point between those two theoretical prices it would raise $512 million and be valued at $3.85 billion. It won’t finally price until October 11.

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As I’ve recently written, people are throwing around the term Big Data as if they’re speaking of duct tape—you can use it here, you can use it there, you can use it anywhere.

This promiscuous use of the term has led to some amusing “findings” in today’s headlines—eg, “60% of companies expect to invest in Big Data dramatically within three years” while at the same time “85% of Fortune 500 companies unable to leverage Big Data for competitive advantage.”

The latter quote is from Gartner, so is easily discounted—besides, there should be an automatic penalty of having to watch The View on a continuous loop for a year whenever one utters the phrase “leverage for competitive advantage.” The first of the two quotes is one of those wildly general, unfocused nonsense predictions that mean nothing. The use of the word “expect” makes it even less convincing.

So how do we restore some order here? Perhaps a little checklist can help.

Are you considering Big Data for a project, an initiative, or a makeover?
Are you with a Fortune 500 or similarly large company?
Is your data new and unstructured? How much do you know about Hadoop?
Is your data traditional and structured? How much of it do you have?
Are you allowed to go outside the company for public-cloud instances? (See New York Times and its creation of 11 million pdfs during one large public-cloud session.)
How many of your current vendors are touting cloud computing? How many are touting Big Data? How many of them did so at your prompting, rather than theirs?
What’s your timeframe?
What’s your budget for using new resources, even if they’re rented, cloud resources?
Why are you considering Big Data in the first place? Have you seen an answer to a longstanding problem, or are you just reading too many articles about it?

I think it’s fair to say Big Data can run anywhere from a gigabyte on up, depending on the size of your organization, your problem, and your previous ability to solve your problem. This statement may sound horrifying, as Big Data has until recently been the province of people who work with hundreds of terabytes and dozens of petabytes.

Facebook claims 100 petabytes (ie, 100 million gigabytes) in its Hadoop-driven repository—the mind boggles at the uselessness of that information for anything other than hawking targeted potpourri. You will likely have much less data, but perhaps at least as serious a use for it.

In any case, I never trust predictions of “60% of this” or “85% of that” or whatnot. We are all as unique as the challenges we face. We can perhaps validate our concerns by reading of other people’s problems and solutions, but can only learn about working this stuff best by doing it on our own, whether we’re part of the 60 percent, the 85 percent, the 99 percent, or the 1 percent.

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Choosing how to build a cloud is perhaps the biggest strategic decision IT leaders will make this decade. It will determine their organizational competitiveness, flexibility, and IT economics.
In his General Session at the 11th International Cloud Expo, Gordon Haff, Cloud Evangelist at Red Hat, will discuss the evolution of cloud computing and the options you have to build a cloud. He will also detail an open source approach to cloud computing that includes open standards, choice of infrastructure, and portability.

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About a decade ago, researchers began to study the performance of e-government portals in order to identify best practices. Studies have also focused on identifying the factors that influence the information quality these portals offer to the public. Most of these studies consider only portal outputs, but ignore the resources (or inputs) used to develop and maintain the portal. This is a problem, because when you analyze only the outputs of a system you are only seeing half of the performance picture. In other words, you are only measuring effectiveness and ignoring efficiency. In an era of ever-tightening government budgets, we simply cannot afford to ignore efficiency in government services. Naturally then the question is: how do you measure the efficiency of e-government portals?

The answer lies is a little known type of analysis called Data Envelopment Analysis (DEA). Once viewed as tool only for serious economists, DEA has experienced an incredible surge in popularity in measuring the efficiency of systems, business units, and processes across dozens of business sectors. In fact, a quick search of Google Scholar yields thousands of published applications of DEA for this very purpose. While the details of DEA are beyond the scope of any blog post, I can provide you with the main idea of DEA and the challenges of trying to measure efficiency without it.

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