Think about how many other cloud database services, platform-as-a-service offerings and — if you can count that high — software-as-a-service applications have launched in the past few years. If the 75-percent-of-all-startups-fail statistic applies equally to cloud computing as it does to other sectors,  we’re about to see a lot more sad emails to users warning them to move their data or find a new provider within the next month.

It sucks to think of adopting a new, presumably useful service as a significant risk, but that’s exactly what is if your data is trapped in some proprietary format or can’t be easily exported. The tide may be turning, though.

We can’t recreate cloud services, but maybe we can extend their lives

According to Mike Driscoll, founder and CEO of cloud-based analytics service Metamarkets (see disclosure), one of the major problems with cloud services is today is that they’re just not designed to be easily replicated. This creates problems when customers — particularly large enterprises — approach cloud providers with contractual conditions that harken back to the era of actual on-premises software. Essentially, they want the cloud version of a software escrow account that would place the service’s source code with a trusted third party and, should the company cease operations, would allow the customer to keep running the service on its own infrastructure.

For now, the response has been to push back on those requests because it wouldn’t really be possible to run the service anywhere other than where it’s currently running. Driscoll said many SaaS applications today — his own included — are “fairly monolithic in the way they’re architected,” which means there’s a strong dependency between the applications and the cloud operating system on which they’re running. He thinks it’s possible that hybrid cloud deployments could help solve the problem (e.g., what OpenStack, Cloud Foundry and Amazon-Eucalyptus theoretically would allow for), but that a feasible hybrid model is probably a few years out.

However, services like Metamarkets, also require a centralized data model (a la Bloomberg terminals) so much of the value is lost if customers all run their own versions on their own servers. For situations like this, he’s heard it proposed that service providers could put cash rather than software into an escrow account, and the cash would pay for a skeleton crew to manage the service for a year, let’s say, so customers would have ample time to find an alternative.

A screenshot of the Metamarkets service

Until things some of these mitigation strategies get figured out, it’s probably more of the status quo for cloud adoption. Small businesses will likely assume more risk and rely heavily on cloud services, while larger companies will use them for non-mission-critical applications or when they’ve received adequate assurances of security and stability. “When you’re GE or JPMorgan,” Driscoll said, “you’re never going to create a dependency on any application that can just get unplugged.”

How insurable are you and your cloud provider?

Maybe the answer is to adopt but protect. I used Xeround’s closure as a reason to catch up with CloudInsure, a cloud-ratings firm that I first covered as it was just starting in 2011. The idea behind the company is to serve as an actuary for insurance providers that want to get into the business of insuring cloud computing customers like they previously have with managed hosting customers and general purchasers of IT equipment.

The way it works is by analyzing some 140 factors about both the user and the cloud provider(s) in order to assign a risk score. So, a high-risk user (e.g., one with highly regulated, very valuable data) might cost more to insure even though its cloud provider is rated as a very low risk. The inverse could be true, too, where a low-risk user could choose to deploy on a high-risk cloud service. Founder Drew Bartkiewicz said CloudInsure covers IaaS, PaaS and SaaS providers, and the financial stability of the provider is among the variables its models consider.

Depending on the insurance policy, insured companies would receive monetary remunerations to mitigate against an outage, breach or closure that required them to pay penalties to customers or regulators, or to move to another cloud provider. Insurance broker Lockton is already offering a cloud insurance product through the International Association of Cloud and Managed Service Providers, and has a partnership in place with CloudInsure, as well.

CloudInsure has solidified quite a bit since we last spoke, established some significant partnerships and, Bartkiewicz told me on Thursday, is about ready to make its service a lot more public.

The insurance model could prove to be a really big deal, especially if it helps smaller cloud providers gain a foothold that will allow them to flourish. Right now, a prudent CIO might decide to opt only for services from companies he assumes aren’t going anywhere — Amazon Web Services, Microsoft, IBM and the like — when insurance might make it a little easier to take a risk on something that might pay bigger dividends.

Besides, it’s not as if being part of a large vendor is always a sign of stability: VMware bought and then sold an app-development technology called WaveMaker in a two-year timeframe, but it just as easily could have killed the business rather than try to sell it. I have reached out to Amazon Web Services to discuss the circumstances under which it would ever consider terminating a service, but have not received a response.

The internet never* forgets

When you look at the topic of web service closures beyond business applications, you actually see just how perplexing and possibly problematic it is. Screenshots might exist of services such as Google Reader and Posterous, but myriad dependencies on other services might make them impossible to recreate even if you had the source code. Unique file formats and other development decisions could present problems for digital archivists trying to preserve the web in a way that’s accessible by future generations.

“This is a case where the internet is more forgetful than the things that came before it,” Driscoll quipped. “The internet never forgets, until it does — and then it forgets everything.”

Disclosure: Metamarkets is a portfolio company of True Ventures, which is also an investor in GigaOM. Om Malik is also a venture partner at True.

Feature image courtesy of Shutterstock user Tom Baker.

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You probably think I’m being sarcastic, but I’m not. Statistically speaking, football teams should go for it more often, they shouldn’t run on third and long and they’re almost certainly better off going for two-point conversions. The guys behind Statwing laid it all out in a blog post on Monday. What’s more, they’ve uploaded an entire data set of NFL statistics to their service that users can play around with for free to analyze a huge number of occurrences and correlations.

From the blog. One of countless analyses available with the data set.

It’s all about democratizing data

Statwing, you might recall, is one of the “data for dummies” tools I highlighted in a January post about advanced analytics tools so simple anyone can use them. Right now, it’s one of the simplest there is. Here’s how I described Statwing then — although it actually performs more types of analyses:

“You upload data, check the variables you’re concerned with, and it plots their relationship. (It also can describe the variables by highlighting the sample size, minimum, maximum, mean, median and standard deviation.) Graphs are accompanied by explanations as to how strong the correlation is based on various statistical metrics, as well as the results of a linear regression model.”

Greg Laughlin (courtesy of his Twitter profile).

The ease of use is by design, says Statwing co-founder Greg Laughlin. “There’s a general zeitgeist that people should care about data now,” he told me during a recent call, but they don’t always know to get started or really even see how all the hype around data relates to them. Early on its existence, Statwing is trying to answer both of those concerns by building an easy-to-use service that also happens to teach users about statistics, and by offering up some interesting data sets for people to play around with.

The latter part is easy, but valuable. Data sets like the NFL data or one about the Titanic’s passengers let other people into the data game and get them thinking statistically. They get people saying, “‘Oh, I grok that. I see how this interesting, I see how this is useful,’” Laughlin explained.

Building a data-analysis service that’s actually usable by mere mortals is a bit tougher. At its core, Statwing relies on a rules engine that considers the type of data uploaded and the types of variables (a maximum of two right now) a user wants to relate to each other. It can handle between 10 and 15 different analyses right now depending on how one defines them, Laughlin said, but at any rate they’re the ones used most often.

He credits Cloudera co-founder and Chief Scientist Jeff Hammerbacher (with whom, along with Greylock’s DJ Patil, I’ll be doing a fireside chat at Structure: Data on Thursday) with helping Statwing decide to make the rules engine the service’s core.

That has been a wise decision because it lets lay users get what they need out of the service without worrying about the underlying functions. Statwing has users that never click the “advanced” tab that shows the statistical breakdown, Laughlin said. They just use the service, essentially, as a faster way of making charts than using Microsoft Excel, and the headline stating whether or not there’s a statistically significant relationship is all the info they need.

“That’s really exciting for us,” Laughlin said. “… It’s giving them the power of stats without them having to think about it.”

Just one view of how Statwing presents results.

Paying the bills with bigger users

Of course, a startup can’t survive on free and unsophisticated users alone, so Statwing is ramping up its money-making efforts. For example, it has “just turned on the paywall in a really light way” by “maybe” charging really heavy users, Laughlin said. In the future, though, Statwing wants to add support for more variables and larger data sets (there’s a 5MB limit right now), and perhaps build in some predictive analytics.

“That kind of analysis is really powerful, really extensible,” he noted.

As the service grows, Laughlin sees the ideal paying user being someone who currently has to use statistical-analysis software like SPSS or R, but who doesn’t really go beyond the basic functions. That type of user has real business need for the software, he explained, but they don’t need all the complexity and arcane statistics dressing that comes along with that that type of product.

Some people don’t want advanced analytics democratized, Laughlin added, because they think people can’t ask the right questions. On the contrary, Statwing’s theory is that most people just struggle with the logistics of cleaning and formatting data and then knowing the terminology associated with the business questions they want to ask.

Back to football …

But forget business users — when will football coaches start caring about statistics?! Maybe not any time soon. Laughlin said a friend of his who works on the MIT Sloan Sports Analytics Conference sees a lot of interest in analytics from the higher levels in sports organizations, but noted that anecdotal evidence suggests most coaches aren’t too interested in letting data influence their decisions too heavily.

Think of a situation like fourth down and goal on the two-yard-line as akin to a CIO choosing between Oracle and some new whizbang database. Nobody ever got fired for buying Oracle, and nobody ever got fired for kicking a field goal.

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This formed the bulk of the €9.2 billion “digital part” of the Connecting Europe Facility (CEF), a part that got trimmed down to €1 billion in budget negotiations. That remaining billion will now go to a different subsection, to do with nailing down cross-border digital services such as e-procurement and e-invoicing. The whole ambition of using €7 billion in EU funding over the coming seven years to accelerate deployment of fibre-access broadband networks — which was in turn supposed to help businesses take up cloud services – just went bye-bye.

The cut could potentially hit rural areas and small towns the hardest. However, digital agenda commissioner Neelie Kroes, who must be bitterly disappointed as she’s worked on the CEF plans for years, issued a statement in which she said she was still shooting for the goals of having half of Europe surfing at more than 100 Mbps, and the rest on at least 30 Mbps, by 2020:

“It is clear that there can be no support for broadband with a pot of only €1bn, so this funding will be exclusively for digital services. Our 2020 fast broadband targets, agreed by everybody, may be harder to reach but I am not giving up on them. I will keep fighting, and I will support innovations that help roll-out fast broadband to underserved areas.”

Kroes went on to warn that member states would now need to rigidly adhere to her recently-announced 10-point regulatory plan for upgrading Europe’s broadband infrastructure, in order to hit targets:

“National governments will not achieve their own ambitions if they fail to offer this support. And their own support schemes will come under great pressure to serve areas where the market alone will not act.”

European telecoms providers had previously begged the continent’s leaders not to cut the broadband part of the CEF, arguing that such a move would harm the EU’s competitiveness. It should also be noted that the European Parliament still has to approve the budget before it can come into force.

UPDATE (Monday 11 Feb): It looks like there will be a serious fight over this budget in the European Parliament. From a statement issued by several blocs of parliamentarians:

“… We cannot accept a budget based solely on priorities of the past. We must maintain support for future-oriented policies, strengthening European competitiveness and research.”

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With that in mind, here are six of the best free tools I’ve come across for helping we mere mortals analyze our data without having to know too much about, well, anything (I’d keep an eye on the still-under-wraps Datahero, too). I’ve gathered some personal data and tracked down some interesting public data sets to help demonstrate what a novice can do with them. Someone with more skills can certainly do a lot more, and larger datasets will provide greater statistical significance.

BigML

BigML is to machine learning what Blue Moon is to Belgian ales: a simple approach to something generally more complex — but also rather accessible and good enough to do the job in a pinch. I explained the service more thoroughly in recent post about it being used to generate predictions of Kickstarter success, but here’s how it works, in a nutshell: Users upload and format data (which is actually pretty easy), BigML discovers the myriad relationships between the variables and creates a predictive model, and users enter hypothetical data and receive a prediction.

I’m pretty bad when it comes to entering my data into Fitbit (see disclosure), but I was relatively good for a month this summer as I prepped for the Warrior Dash, and that’s the data I used to demonstrate BigML. This prediction of how many calories I can expect to burn in a day would work a lot better if I had a bigger sample size and hadn’t occasionally forgotten to log calories and hours slept, but you get the point. The first image is the model the service generated; the second is the prediction interface.

Google Fusion Tables

The user interface for Google Fusion Tables  isn’t what I’d call pretty (“sparse” is probably a better description), but the still-in-experimental-mode visualization tool sure is easy if your data is nicely formatted. I created this interactive map simply by uploading a publicly available dataset about gun violence and clicking the button to create a map:

For this simple comparison of gun ownership and gun homicide rates, I just checked the countries by which I wanted to filter the chart. Easy.:

Infogram

If you have really simple data — like a few columns and a handful of rows — Infogram might be the easiest to use of the bunch. The company launched last year with a variety of infographic templates, but it has since expanded to include a large number of charts and graphs, too (including line, pie, pictorial, treemap and bubble). Furthermore, it gives sample data, which you can use as an example to enter your own or format the table you want to upload, and the interactive charts embed nicely into web pages (ours, at least).

Here are the top 10 things I ate during the time I was logging food via Fitbit, excluding copious amounts of beer, water, coffee and Diet Pepsi that I didn’t record.

In July, I made this chart with Infogram comparing infrastructure spending trends among internet companies.

And here’s a sample of the simplest chart in the world.

Many Eyes

Many Eyes is a free web service run by IBM that includes a wide variety of visualizations ranging from maps to pie charts to scatter plots. But what makes it stand apart from the others is the suite of text-analysis tools it offers — not only are they fairly novel, but all they require users to do is paste a page of plain text into the web interface and press a button to visualize it. I used it to analyze the last 15 posts I’ve written for GigaOM.

What did I find? For starters, I use the words “data,” “Facebook” and “users” a lot.

When it comes to two-word combinations, “big data,” “data centers” and “hard drives” are among the biggies.

This one is particularly interesting, showing how I tend to form phrases around certain words with common conjunctions, or just a space, in between.

Apparently, out of 10,013 words, I only used “cloud” 20 times. I usually followed it up with “provider,” “servers,” “computing,” “-based” and “providers.”

For fun, I also made a word cloud based on couple month’s worth of Fitbit food logs. It turns out, you can take the boy out of Wisconsin, but …

Statwing

Statwing might be my favorite of the bunch, if only because it’s so simple yet actually tries to teach users about statistics. You upload data, check the variables you’re concerned with, and it plots their relationship. (It also can describe the variables by highlighting the sample size, minimum, maximum, mean, median and standard deviation.) Graphs are accompanied by explanations as to how strong the correlation is based on various statistical metrics, as well as the results of a linear regression model.

To demonstrate Statwing, I went back to the Fitbit data. Of the variables that Fitbit tracks, some correlations are easy to predict (e.g., steps and calories burned), but I was kind of surprised to see that the 86 minutes a day I spent being fairly active really weren’t that good of an expenditure of my time.

Tableau Public

Tableau Public, the only free version of the popular business-intelligence software, was clearly designed with business users in mind. It expects a lot of structure in the data, and although you can edit almost every aspect of it within the application to get it into usable shape, the service doesn’t allow much guidance if you don’t speak the language of BI (it also requires Windows). But the software is very good at deciphering the characteristics of different variables, the drag-and-drop operation makes it kind of easy to experiment and the wide array of visualizations look really nice.

Using my Fitbit data (and here’s where you see how lax I am at data entry), I created a line graph comparing the calories I ate each day with the calories I burned. Assuming I didn’t go crazy eating on the days I forgot to make entries, the good news is I never ate more calories than I burned. (Note: Although these are static images, Tableau Public actually lets you embed interactive charts, which I’ve used in the past on several occasions, but they don’t always fit well within our pages.)

Here’s one I played around with a while back charting Amazon’s “Other” revenue againt the number of objects stored in Amazon S3.

Finally, here is my first-ever (I think) Tableau chart, which uses the raw data on government takedown requests that Google provided along with its Transparency Report in October 2011. You can read that post and play with the interactive version here.

There is, however, one disclaimer that applies to all of these tools: I didn’t get into cleaning and formatting data, which can be a somewhat arduous process. Many tools expect some sort of structure to the data — the X axis to be in columns and the Y axis in rows, measurements without units (e.g., grams), etc. — that just isn’t present if you’re downloading an Excel or CSV file rather than creating it yourself. Sometimes, with comprehensive datasets like your Fitbit Premium data, you’ll have to separate or combine the relevant data into new spreadsheet tables before uploading it to a service. But once you have the data ready to go, these tools can help you analyze it, visualize it and hopefully glean some insights from it.

Disclosure: Fitbit is backed by True Ventures, a venture capital firm that is an investor in the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.

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Funambol won’t just get the $5.75 million cash infusion -– its last round in 2008 raised $12.5 million -– it will also have access to a working capital line, which it will use to expand into new international markets. The company started out as a cross-platform mobile e-mail synchronization service, but it’s evolved into a white-label cloud services company that lets wireless and wireline operators, ISPs and device makers to launch their own branded cloud storage services.

It’s biggest growth, however, has been among mobile operators who are pitting Funambol’s platform against the growing number of “siloed” clouds that limit storage to particular set of devices, operating systems or apps. Funambol and its carrier partners figure that they can carve a niche for themselves by opening up cloud content storage to any device or platform.

Funambol has already signed deals with Sprint, Clearwire, Telefónica, BT and a handful of smaller international operators, but it’s biggest customer is still unnamed. Funambol claims that customer is among the global top 5 in both customers and revenues. The only operator to fit that definition besides Telefónica is either China Mobile or Vodafone.

Feature photo courtesy of Flickr user getinet

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The email highlights GoDaddy’s focus on winning small business customers and the apparent ill fit of Cloud Servers for that goal. “After reviewing all of our hosting products, we decided to double-down on our shared hosting and site builder products and invest to win in these spaces,” it reads. “As part of this focus, we will discontinue Cloud Servers as a stand-alone product.”

GoDaddy has confirmed the decision in an email to me via a statement from CIO Auguste Goldman:

 ”We are focused on SMBs and SMBs don’t use this product the way we are offering it now. So, in the weeks ahead, it won’t be a stand-alone product in and of itself. However, we plan to continue developing cloud technology into our other hosted products.

“We will continue to support existing Cloud Server customers in a variety of ways.”

This type of decision is one of the classic risks of cloud computing, where a service might be here today and gone tomorrow. Services rarely shut down immediately, thus leaving users without a chance to reclaim their data or code and move it elsewhere (well, except in the case of MegaUpload), but an eventual transition to a new service is still necessary nonetheless. Recent examples of cloud services shutting down include Motorola’s ZumoDrive cloud-storage service, the Slicehost on-demand servers service (by Rackspace) and, nearly, TextDrive (by Joyent), which was saved at the last minute.

The GoDaddy email mirrors Goldman’s statement on this issues, saying the company is “identifying the best ways to support customers who need to transition from Cloud Servers. Once we identify these support paths, we’ll notify them of the product update.”

Feature image courtesy of Shutterstock user Lucie Lang.

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Supporting this view, Gregory Mone argues in the new Communications of the ACM that “leading companies have begun revising the way they design, maintain, and monitor data centers, from the physical building all the way down to the hardware…” While the industry attempts to balance inexorable economic forces with social and environmental responsibility, I’d like to view the debate from a broader perspective: perhaps we’re focusing on the wrong question.

Rather than focusing on the percentage of global energy that data centers use, suppose we focused on whether there is a net benefit in the use of IT, expressed and mediated through computing in data centers and the cloud. After all, if one knew the winning lottery numbers, one wouldn’t begrudge the dollar for the ticket purchase versus the millions in winnings. A variety of mechanisms would seem to generate net benefits: substitution, optimization, innovation, location independence, virtualization and multitenancy, and dynamic resource allocation.

For example, IT can deliver a net reduction in energy use via substitution. Examples abound. Conducting an audio or video conference, mediated by cloud data centers and cloud-resident multipoint conference bridges uses energy, but substantially less than the gasoline and jet fuel that would be required to drive and fly the meeting participants to their destination.

By one estimate, each meeting held this way can generate an average net benefit of nearly a half metric ton of greenhouse gases. Streaming a video out of a content delivery network takes energy, but less than the amount required to drive to and from the neighborhood video store, in the worst case for both pickup and return. Perhaps this is why data centers are responsible for less than 1.5 percent of global energy use, whereas transportation uses a reported 25 percent.

IT can also deliver a net reduction in energy use via optimization. UPS for example, has reportedly reduced both miles of delivery routes and tons of CO2 emissions by tens of millions using “package flow” software. In one recent study, mere feedback on energy consumption decreased energy use by several percent on average, but up to 25 percent among the “cybernetically sensitive.” Smart grids have been projected to reduce peak power requirements and thus achieve 4-to-6 percent efficiency improvements.

A related benefit is innovation. Compute-intensive simulation modeling is required to improve jet engines, automotive aerodynamics, molecular dynamics for drug discovery, and so forth, in virtually every domain of technology. And, open contests and innovation markets such as Innocentive leverage what Don Tapscott calls “networked intelligence” to accelerate collaborative innovation and crowdsourcing. These innovations, in turn, generate net benefits.

Location independence, a key attribute of cloud services, means that processing can be done where power is cheap. Restated, this means that relative prices, which express relative costs of generation plus costs associated with transmission and distribution losses, can signal globally optimal locations for processing and storage of information.

Virtualization and multitenancy drive additional efficiencies. Cloud providers don’t maintain a dedicated server for you personally on the off chance that you might want to send or receive an email. Rather, in the same way that thousands of people can enjoy Central Park, thousands of people can send and receive email via a single server, each using a small fraction of the available resources, generating orders of magnitude of efficiency. In the case of at least one SaaS provider, it’s been calculated that there can be a 65-fold savings Stanford’s Jonathan Koomey concludes that lower than projected growth rates in data center electricity use over the past few years are due in large part to adoption of virtualization.

A related efficiency gain in the cloud is due to dynamic resource allocation. In the same way that a hotel allocates and reallocates its rooms over time, a cloud provider can allocate and reallocate compute, storage, and network resources. When individual customer demands are independent, utilization can increase to nearly 100 percent, especially when given a boost by dynamic pricing. This is how the MGM Grand can achieve a reported average occupancy rate for over 5,000 rooms of 96.8 percent. Daily usage fluctuations impact utilization driven solely by interactive user demand, but this is where yield management and dynamic pricing through mechanisms such as spot instances can enhance overall cloud utilization.

Andreesen-Horowitz’s Mark Andreesen has famously argued that “software is eating the world,” and Kleiner Perkins Caufield Byers’ Mary Meeker has chronicled the “re-imagination of nearly everything”—news, photography, diaries, communication, etc.— to an Internet / cloud based model. The increasing transformation of the global economy from atoms to bits means that global productivity growth and therefore human welfare increasingly depend on harnessing IT, whether for the common good or for competitive advantage. And, yes, IT uses electricity. However, like preventative healthcare or education, in addition to optimizing the investment, perhaps we also focus on the return.

Joe Weinman is a senior vice president at Telx, the author of Cloudonomics: The Business Value of Cloud Computing, and a regular guest contributor to GigaOM. You can find him on Twitter @joeweinman and he’ll also be speaking at our upcoming Structure:Europe event in October.

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Called the Analytical Dataspace, 1010data’s new offering is like the Walmart of data marketplaces in that it brings together everything you need for valuable analytics — data, applications and even some customer service. Or maybe it’s like the Goodwill of data, in that the data sets are used, but not used up. However you choose to characterize the Analytical Dataspace, though, it’s probably a good idea and probably a sign of things to come.

When people talk about the three Vs of big data — volume, velocity and variety — the latter can be more of a benefit than a challenge to overcome, although finding the right blend of data sources isn’t always possible. We’ve seen data marketplaces pop up and succeed to varying degrees, but their products tend to target application developers wanting API access to data, and the data sets themselves can be of questionable utility for many enterprises. In theory, 1010data’s new platform should help solve these problems because its customers are typically larger businesses with data that could actually be valuable to other organizations. Already, the company hosts a number of data sets covering everything from home prices to financial markets to weather.

It also doesn’t hurt that these data sets reside on a platform that also includes applications to analyze them. There’s 1010data’s general-purpose service, but also forthcoming industry- or application-specific services from software partners that will sit atop that. After all, all the data in the world is only useful if you know what to do with it, and 1010data thinks a collection of analytics applications will take away any guesswork.

The Analytical Dataspace is just a step in the evolution of data-centric communities, however. While it advances and puts an enterprise spin on the work of companies such as Factual and Datafiniti (and even Google’s Fusion Tables, to a degree), it’s not quite the nirvana that Cisco envisions with data infomediaries serving as the hubs for all the big data needs of entire industries. It’s not quite clear where, or whether, these types of communities or brokerages will catch on, but one has to expect there’ll be some traction if there’s a profit to be made.

Feature image courtesy of Shutterstock user JeremyRichards.

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Since coming to NEA out of VMware about eight years ago, I have been intensely focused on early-stage enterprise technologies, and my partners and I saw the early promise in PaaS, investing in companies like Engine Yard and Apprenda. The space is getting even more interesting as it evolves, with the rapidly proliferating services-based approach to cloud applications creating a host of new challenges — many of which can best be addressed by PaaS vendors, both public and private.

Developers are increasingly assembling applications in the cloud by tying in APIs provided as a service, rather than coding the applications from scratch or linking in code packaged into libraries. Numerous startups have emerged to provide these APIs to developers, such as Twilio for messaging, SendGrid for email and Stormpath (which NEA invested in earlier this year) for user security management. All of these cloud service providers enable developers to focus on the core logic that makes their application unique, rather than developing functionality that is common across applications. Yet while the explosion of cloud services has clearly been a boon to developers, it presents a new set of challenges in terms of discovery, implementation and management.

If I’m developing a new application, how can I find the most reliable, secure services that are compatible with my run-time environment and data store? Once I’ve implemented these services, how I can be sure I’m using them appropriately and cost-effectively? Furthermore, if I decide to offer the core functionality of my application to the public as a service, how can I ensure this service is discoverable, trusted and usable by other developers?

PaaS vendors, public and private, are in a perfect position to solve these challenges by sifting through the numerous available cloud services, curating those that are the most beneficial and trustworthy, and promoting these services to developers. Thousands of developers are already paying these vendors to help them build, manage and deploy applications, so the “chicken and egg” problem of creating a new marketplace has already been solved. They are a trusted partner. A PaaS also happens to be one of the first decisions a developer makes when building a cloud application, so it’s a natural extension for these PaaS vendors to curate third-party services to increase the value proposition of their platform to developers. Leading public PaaS vendors such as Azure, Engine Yard and Heroku, as well as private PaaS such as Apprenda, have a tremendous opportunity to lead the industry in cloud services curation.

You may ask, what about Amazon? As an Infrastructure-as-a-Service (IaaS) provider, they are primarily motivated to appeal to as broad an audience as possible in order to drive more compute cycles. This has caused them to open up their marketplace for the entire ecosystem, rather than curating an offering suited to a specific category of developer. In addition, it is no secret that third-party software developers are starting to trust them less and less. Amazon has gradually expanded upwards from the IaaS layer, adding on PaaS-like capabilities which compete directly with partners in the Amazon Web Services (AWS) ecosystem. As long as the fear persists that Amazon will take the best services in house, developers will be wary of relying on them as a channel to distribute their cloud services. The truly independent PaaS players who empower rather than compete with their developers will prevail.

The implications of cloud services curation for startups, PaaS vendors and enterprises are significant:

• New companies that sell cloud services will increasingly go to market through PaaS vendors, greatly decreasing sales and marketing expense. As Apple’s App Store is to mobile independent software vendors (ISVs), marketplaces for PaaS vendors’ services will be to cloud service vendors.
• PaaS vendors will offer new services to guarantee security and cleansing to ensure that everything on their platform is compatible, reliable and trustworthy. They will leverage their position as a trusted vendor for developers to make the most of this new way to develop applications.
• Enterprises will win big. They only need to standardize on one trusted PaaS rather than individually certifying each cloud-based service. They can pay and manage numerous cloud services through one vendor. If anything does go wrong, they will have the support of an established partner to ensure the problem gets fixed quickly.

Ultimately, cloud services curation is likely to be provided as an additional offering by the handful of leading PaaS vendors, rather than stand-alone marketplaces or IaaS providers. In a world with an ever-expanding number of cloud services, curation will help developers discover the best services and help the best services achieve broader adoption — thus bringing innovative and secure applications to market faster than ever before. We expect cloud services curation to be one among several rich strategic opportunities on the horizon for PaaS vendors as the PaaS market transitions from nascent to mature.

Peter Sonsini, general partner at NEA, invests in early-stage enterprise software and consumer companies. In addition to the PaaS companies mentioned above — Apprenda, Engine Yard and Stormpath — his current investments include BeachMint, Conviva, Embrane, Eucalytpus Systems, Lithium Technologies, MapR, Splashtop, Tintri and Viddy. Peter’s previous investments include Xensource (acquired by Citrix) and Teracent (acquired by Google).

For more on the evolution of PaaS, attend GigaOM’s Structure:Europe conference in Amsterdam on October 16 – 17.

Image courtesy of Flickr user Materials Aart.

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BigQuery, which became generally available in May, is a cloud service that lets users run SQL queries against terabyte-scale datasets and get answers in just seconds. However, as BigQuery Product Manager Ju-kay Kwek wrote in announcing the new features, “[W]e understand that there are important, non-interactive queries, such as nightly reports, that businesses also need to run. Now, you can designate a query as a batch query and it will complete within a few hours.”

The slowed-down queries also come with a lower price — 2 cents per gigabyte versus 3.5 cents per gigabyte on interactive queries.

And where batch queries address the need for more-traditional use cases, adding the Excel connector addresses the need to bring in more-traditional users. Excel is still many analysts’ preferred method of working with data, and products that understand this — whether it’s a Hadoop cluster or a machine-learning service — seem destined to attract more users than those that force analysts into using an entirely new tool.

These new features are also evidence of the new (and arguably improved) Google Enterprise division. Cloud services, super-fast answers and web scale are great for showing off what Google is capable of and pointing toward the future, but making money off a product might mean having to bring it back down to earth to address the needs that most potential users actually have today.

Feature image courtesy of Shutterstock user olly.

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