Investor Steve Jurvetson next to a pulse fridge that cools a D-Wave quantum computer

As you can see in this photo from venture capitalist Steve Jurvetson, D-Wave uses a pulse fridge to cool the quantum computer to .02 degrees above absolute zero, and they use Helium-3 in the cooling process.

Quantum computers use a different type of processing compared to traditional computing. As GigaOM’s Jordan Novet explained it earlier this year, “rather than working with binary yes-or-no questions — ones and zeros — quantum computing is more probabilistic, also allowing a combination of zero and one to simultaneously answer many questions with quantum bits of information, or qubits, and tell users more about the likelihood of a situation. It’s not necessarily useful for all kinds of computing, but it could solve problems that current computers can’t.”

Keeping quantum computers that can perform such functions cool can be a tricky process. It’s highly energy intensive and can get expensive. But if the quantum computers are not cooled down, the molecules — which are being manipulated to store data — move around chaotically and can’t be manipulated and read.

Earlier this year physicists at UCLA developed a new cooling process that immerses charged barium chloride molecules into a super cold cloud of calcium atoms. That research is being funded by the Army Research Office and the National Science Foundation.

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Arment says:

“Anything that is really sensitive or extremely valuable or needs to be kept very secret, I wouldn’t store on anybody else’s servers. That, to me, seems ridiculous unless I held the encryption keys like with the online backup service that I use.”

Public cloud-based locker services hold the keys to encrypt and decrypt your data on their servers. Why? It’s important to remember they are used as a collaboration tool. To offer public file sharing features, they need to decrypt data stored on their servers. In addition, employee could view or steal your data. To make matters worse, public cloud services are more likely to be hit by hackers because they are high value targets.

However, there is an alternative. Varonis DatAnywhere is a private cloud-based file sharing solution that is secure and easy to use.

• Create a secure private cloud experience using your existing file sharing infrastructure
• Keep your data on your file servers
• Keep your existing permissions (e.g., NTFS and Active Directory)
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Keeping data isolated inside any one database prevents companies from making discoveries across multiple data sets. Plus, he said, at least one database tends to have trouble at any given time.

Earlier this year, Falco started Orchestrate.io to respond to these issues. The company provides a single API through which customers can send data from multiple databases. This way, customers can join, say, geolocation data, time-series data and tweets, drawing graph relationships and doing full-text searches on top of it all.

To build out the infrastructure to do this with multiple cloud providers and bring on customers, Portland, Ore.-based Orchestrate.io is taking on $3 million in seed funding. True Ventures is leading the round (see disclosure) alongside contributions from Frontline Ventures and Resonant Venture Partners.

Some companies were already testing out the Orchestrate.io service, although Falco declined to identify them. He said the price of using the service is tied to the number of queries per second customer make.

When it comes to competition, Falco said, “Certainly there’s Amazon.” On Amazon Web Services, customers can get a slew of tools, from RDS for relational databases to DynamoDB for nonrelational work to Elastic MapReduce for Hadoop. And, of course, if companies don’t buy into the Orchestrate.io logic, existing databases constitute challengers. But Falco has an answer for that. “Databases can do most of these queries,” he said. “The problem is, they can’t do them efficiently and at scale at the same time.”

After the company comes out of private beta, Falco thinks Orchestrate.io has the potential to be a go-to provider for lots of different kinds of data-analysis services, Falco said, just as companies look to Twilio for voice services and SendGrid for email. “(There’s a) shift of operational burden from a corporation or the end user to a service provider,” he said. “I think we’re just part of the trend. You’re going to continue to see that over the next several years.”

Disclosure: Orchestrate.io is backed by True Ventures, a venture capital firm that is an investor in the parent company of GigaOM. Om Malik, founder of GigaOM, is also a venture partner at True.

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Tuesday at the IBM Smarter Commerce Global Summit in Nashville, Tenn., IBM plans to launch the Watson Engagement Advisor, aimed at helping consumer brands better recommend products to customers and provide better customer service at scale. Yes, that’s right. This is a technology that can diagnose cancer will be used to help sell people more products.

Why we need Watson-level AI for customer service today.

Still, customer service is a legitimate and complicated problem, especially in an era where social media meets our desire for a personalized and instant response to any inquiry or service issue. Firms have to engage with customers via phone calls, tweets, Yelp, Facebook posts and for all I know, angry letters. And many of those customers using new media don’t want to wait for a response. Companies that can offer good service quickly in a variety of mediums have an advantage. And Watson would allow them to do this at scale. Imagine offering Ritz Carlton service at Holiday Inn prices.

Brands who buy the Engagement Advisor software will get access to a much smarter virtual agent that can sift through massive amounts of information to respond to users’ questions quickly. As someone who was totally schooled at Jeopardy by Watson, I cannot emphasize enough how fast it is.

The IBM release notes that the Engagement Advisor software is designed to help existing customer service personnel answer questions quickly or it can be deployed via the brand’s mobile site where customers can interact with Watson directly. As IBM’s release says, “In one simple click, the solution’s “Ask Watson” feature will quickly address customers’ questions, offer advice to guide their purchase decisions, and troubleshoot their problems.”

It’s possible this will remind users of Siri, Apple’s chatty personal assistant on the iPhone and iPad. However, instead of being deployed on a device, Watson is embedded on a brand web site.

It can greet customers by name, however and offer to help them via a chat window on the company site or via a mobile push alert, that will appeal to people who want to tweet or text their customer care questions without having to stay focused on a single web page. In the ideal case Watson will have access to customer records plus the data stores it was trained on, and will be able to use both in giving a customer a recommendation or help.

The business of Watson is a big one

Instead of naming customers directly IBM writes that brands including ANZ, Celcom, IHS, Nielsen and Royal Bank of Canada are, “exploring how the Watson Engagement Advisor can help them engage with their customers.” This may be phrased this way because the initial pilot projects involving Watson require a lot of training of the computer before it becomes valuable. During Watson’s “apprenticeship period,” IBM in some cases hasn’t charged clients, or charges them lower rates.

But it’s no secret that Watson is a big business bet for IBM. At last year’s Structure conference, Dan Cerutti, IBM’s VP of Watson Commercialization, explained IBM’s ambitions for Watson, including delivering the machine as a service over more and more devices. IBM sees Watson as a new type of computing and plans to build out new business models to support it, as Cerutti detailed in our chat almost a year ago.

Along the way Watson not only impresses with its ability to filter through reams of data to correctly answer a natural language question, it also has been able to do this as it shrinks in size. Since its television debut, Watson has seen a 240 percent improvement in system performance, and a reduction in physical requirements by 75 percent. The whole system can now be run on a server that takes up the size of four pizza boxes from a giant machine that took up an entire bedroom. Smart, svelte and delivered as a service. Get ready to meet Watson in more roles and in some surprising places.

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From the press release announcing the findings:

“If a robot just sticks out its hand blindly, or uses motions that look more robotic than human, a person might feel uneasy working with that robot or might question whether it is up to the task,” Katsu Yamane, Disney Research, Pittsburgh senior research scientist explained. “We assume human-like motions are more user-friendly because they are familiar.”

Despite the robot pictured on the Disney page touting this research looking like the mechanical, blue-haired skeleton that haunted my childhood nightmares, its attempts to grab the purse from the person do seem reactive to the human’s gestures, as opposed to the robot just sticking his arm out there and the person having to accommodate it. And that sort of naturalism will be important as we bring more robots into our homes and workplaces.

For example, an MIT group used a dancer’s motions to build a robotic bartender in a quest for naturalism — even though that robot doesn’t interact with people.

Today, designers try to endear robots to us with quirky noises (like R2D2) and maybe light displays or LED faces — anything to help anthropomorphize them. But as robots become more human-looking they can also become more sinister — achieving that same uncanny valley that Disney and other content companies have struggled with in animation. Remember the dead-eyed stars of the Polar Express that you probably couldn’t empathize with? The jerky movements of a home health robot might engender similar feelings — or worse — they may scare people.

Building the natural gestures of the Disney robot took the creation of a hierarchical gesture database that the robot can access as it detects the person passing something to it. In the Disney paper research, the robot is not only able to reach for the handbag, but when the human attempts a fake pass to the robot, the blue-haired monstrosity robot is able to adapt. From the release:

To enable a robot to access a library of human-to-human passing motions with the speed necessary for robot-human interaction, the researchers developed a hierarchical data structure. Using principal component analysis, the researchers first developed a rough estimate of the distribution of various motion samples. They then grouped samples of similar poses and organized them into a binary tree structure. With a series of “either/or” decisions, the robot can rapidly search this database, so it can recognize when the person initiates a handing motion and then refine its response as the person follows through.

Even if you don’t have an opinion on how naturally robots should move, this research brings home the awesome amount of work it takes to build computers and robots that mimic the capabilities of a person. Much like computer visualization, the science of robotic interaction takes a problem the size of a mountain and has to chip it down into grains of sand using a toothpick to find solutions. It’s a testament to human curiosity that people are willing to try.

Also, I expect Disney might be lured by the idea of natural-looking robots roaming its theme parks. My only question is would they be dressed up as characters or working the cash register at the gift stores.

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That’s why a group of researchers from Columbia University and Stanford University developed an algorithm that can do just that, turning those cells into something that resembles your social graph. This lets researchers see how the various cells are related to each other so they know , for example, where to focus cancer treatment and what to track as that treatment progresses.

The idea of representing large or complex data as a graph is nothing new, but it has taken on more prominence thanks to the rise of social media and those ubiquitous social graphs that map out who’s connected to whom. As we highlighted recently, however, graph analysis is becoming more popular outside the realm of social networks, and is being applied to problems that are more complex than just figuring out simple relationships within a network. In cases such as medical research, especially, graphs can provide a very effective way of seeing how potentially hundreds of thousands of data points spanning perhaps hundreds of variables are similar to each other.

That’s exactly what the team at Columbia and Stanford has done with a new algorithm that they’ve demonstrated within the realm of mass cytometry. According to a press release announcing the research (which is available via paid download at Nature Biotechnology):

“The method, called viSNE (visual interactive Stochastic Neighbor Embedding), is based on a sophisticated algorithm that translates high-dimensional data (e.g., a dataset that includes many different simultaneous measurements from single cells) into visual representations similar to two-dimensional ‘scatter plots’ ….

“The viSNE software can analyze measurements of dozens of molecular markers. In the two-dimensional maps that result, the distance between points represents the degree of similarity between single cells. The maps can reveal clearly defined groups of cells with distinct behaviors (e.g., drug resistance) even if they are only a tiny fraction of the total population. This should enable the design of ways to physically isolate and study these cell subpopulations in the laboratory.”

I assume they say similar to scatter plots because the algorithm is analyzing data across more than two dimensions, although the resulting chart is essentially the same (i.e., data points with similar characteristics will form clusters).

The results of viSNE, showing cell densities in diagnosis and relapse samples.

Whether or not they’re technically similar, this research seems similar to what Ayasdi is doing with its new data-analysis software based on a technique called topological data analysis. In both cases, though, the algorithms aren’t necessarily concerned with how data points interact with one another (like in network graphs), but rather what similar characteristics the points share. Ayasdi’s software has been used in cancer research, too, including on datasets spanning hundreds of patients and tens of thousands of variables.

In theory — although not likely in practice considering the complexity of the datasets medical researchers are dealing with — these approaches are similar to clustering approaches that are also popular among data scientists working with web companies. In areas such as e-commerce or email management, for example, where there isn’t a strong social element, companies can broadly break customers into distinct groups based on their behavior or interests.

A sample cluster of MailChimp subscribers.

Of course, curing cancer is a slightly more compelling — and difficult — goal than targeted advertising. The algorithms have to be precise so as not to miss similarities hidden within the mass of data. In the case of viSNE, the researchers say they’ve been able to spot small groups of cells (like 20 out of tens of thousands) that might be able to survive chemotherapy and increase the likelihood of a recurring tumor.

But we probably shouldn’t bee too quick to discount the work that web companies do as somehow less valuable than that of cancers researchers, for example. The big data era arguably started with the web, and web companies have generated some of the most important data-analysis techniques and technologies around today (see, for example, Google’s Jeff Dean, with whom I’ll be speaking at our Structure conference next month). As medical researchers start generating more and more data via cytometry, genome sequencing and even electronic medical records, it will be critical for individuals in all fields to keep track of what data scientists in other fields are doing and figure out how that might apply to their own work.

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Intel Labs is engaged in a research partnership with wethedata.org, a “hub of conversation, news, and events celebrating innovative communities who are each focused on democratizing data in their own way.” Intel also has contributed to a hackathon for building tools consumers can use to understand publicly available data, and it’s sponsoring the National Day of Civic Hacking for getting people across the country to come up with ways to analyze open data sets.

It’s somewhat surprising for Intel to be pushing for data democratization. Intel chips are at the heart of servers THAT companies and government organizations use to crunch heavy loads of consumer data. And Intel also has come out with its own Hadoop distribution for handling big data.

The sort of rhetoric floating around the Wethedata.org site — “we are the customer, but our data are the product. … How do we regain more control over what happens to our data and what is targeted at us as a result?” — seems more likely to come from a nonprofit or even a government agency than from a collaboration that includes a corporation such as Intel. But that might help explain why the efforts are noteworthy.

Intel isn’t the only one active on this front. Andreas Weigend, a former chief scientist at Amazon.com, often raises the topic with executives in his consulting work with big companies around the world, partly because some data is simply wrong, and consumers ought not be penalized for it. And as the MIT Technology Review article notes, legislators have taken stabs at the issue, albeit with little success so far.

Now that Intel is on board, perhaps more tech companies will join in and the prompt the tide to change. And if that happens, interesting questions would arise, such as how exactly companies would roll out more data on its customers, whether companies should give consumers access to algorithms that factor into decision-making and how much visualization software and other tools should be made available.

Feature image courtesy of Shutterstock user Lasse Kristensen.

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President and COO George Mathew says the company’s mission is to be a one-stop shop for statistical analysis. It wants to be the one place where analysts and data scientists can blend their data, model it on it and then visualize it. Often, he noted, that same process might require two or three separate products.

Another feature that Alteryx hopes will set it apart is its collection of prebuilt models in what the company calls an analytics gallery. Users can share their own work or find models others have built for tackling similar issues. Alteryx also offers up its own pre-formatted datasets for analysis, often public data such as the U.S. census.

“The canvas for creating an analytics application should never be blank for the analyst when they’re getting started,” Mathew explained. They often need to understand external data as well as their internal data, so Alteryx’s software gives them easy access to it.

Because it’s based on the R statistical-programming language, heavy R user Walmart has been able to transition some workloads to Alteryx when employees need an easier user experience. McDonald’s uses it to analyze data about franchisees and about its growth strategy in China, and Bloomin’ Brands (parent of company of Outback Steakhouse and other restaurants) is using it to help build menus that take into account what diners in various parts of the country prefer to eat. Nine of the 10 leading top wireless providers providers are also users, Mathew said, trying to blend actual call data with traditional sources such as customer service data.

Mathew compares Alteryx’s current growth as analogous to software-as-a-service applications like Salesforce.com in the CRM space, or even Tableau in the traditional business-intelligence space. In a business world increasingly driven by at least the idea of big data, one might expect any vendor pushing a more consumer-like purchase and consumption experience to get interest from companies tired of dealing with legacy software or never wanting to experience it in the first place.

“The disruption that’s happening is creating a new space for ourselves,” Mathew said, “without having to go head to head, frankly, with the a status quo out there.”

Feature image courtesy of Shutterstock user ramcreations.

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Marketing becomes new revenue arm

Traditionally, CMOs have dealt with the “soft skills” of marketing. They headed up cost centers filled with branding, advertising and campaigns that were expensive endeavors, producing benefits that were often difficult to measure. In this current shift, CMOs might not bring CIOs to their knees, but if corporate budgets could talk they would certainly favor the CMO. To wit: Gartner predicts that by 2017 the CMO will spend more on IT than the CIO. Why? Mobile, big data and real-time analytics are transforming the modern CMO’s organization from a cost center to a critical revenue-driving arm to reach and engage the customer base.

Realizing that CIOs and CMOs probably hate stories of a battle raging between them, there is actually a peaceful, and logical, middle ground in which the two work together to harness the vastness of big data to create real-time – and importantly, actionable – analytics. While the CMO brings the marketing skills to the table, the CIO has the technical chops to deal with capturing, processing and integrating data to make it useful.

Mobile transition requires new tools

The CMO is in a particularly prime position in the increasingly important mobile channel, as the feedback loop is uniquely personal and immediate. The ability to measure, predict and act upon an end-user action has never been more precise.

However, mobile is a different beast and legacy systems designed for the web rely on cookies to track user interaction. As we go more mobile, we enter a world devoid of cookies. New tools are required to match mobile users to their actions across multiple channels. Along with new tools, there needs to be a new partnership – the CMO and CIO united as a Dynamic Duo – working diligently together to deliver the cloud-based and back-office infrastructure required to pull actionable information from big data across all channels: bricks and mortar, web and mobile.

New focus on data-driven outreach

To get a view into what makes the CMO so potentially powerful, let’s turn to another battle, the one that happens on a quarterly basis across corporate America and beyond: The fight to make the quarterly earnings number. Typically, a CEO faced with a sales or earnings shortfall will turn to the CFO and the head of sales and say: “What can you do?”

The honest answer is: short term, not much. They can jam the channel, which will make the next quarter’s number even harder to achieve. Or shovel product out the door at prices that wouldn’t otherwise make sense. But by the time the word gets to the field and actions kick into gear, there can be quite a lag in results. And, again, these results may have value only for the short-term.

In the very near future – one that has already arrived for some organizations – the CEO turns to the CMO, who looks up from his or her laptop and says: “No problem. We should make that number by noon.”

The CFO and head of sales turn and say in unison: “How?”

“While you were talking I found an overstock of our Zing42 and sent an offer to 234,000 customers who have purchased this in the past, and statistically are ready to buy again, and to another 341,000 customers who haven’t purchased this yet, but show a high probability of doing so at the price point we are offering.”

No hurried sales calls to the field asking them to stuff the channel. No broadcasting of sales and discounts that can tarnish an image or weaken a brand. But rather, an analytically-driven offer to a curated digital audience delivered via a simple mobile notification, or in-application offer, to thousands of people who are analyzed and selected to be eager buyers.

This is the look of the CMO ascending: Powered by a cloud of mobile, big data and real-time analytics.

Rob Lilleness is the CEO of Medio, a Seattle-based provider of analytics solutions for mobile computing. Follow him on Twitter @Robmedio.

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Of course, as Om and others have already mentioned, there’s no guarantee this deal will actually be consummated: it could fall apart on valuation, as so many deals do — or Facebook could swoop in with a much higher offer and snatch Tumblr out of Yahoo’s clutches, the same way it did when it stole Instagram away from Twitter last year for close to $1 billion.

Update: According to the Wall Street Journal, the Yahoo board of directors has approved a $1.1-billion all-cash bid to acquire Tumblr.

It makes Yahoo look desperate — because it is

Even if the deal does get done, one of the risks for Mayer and Yahoo is that the company could look desperate by paying more than $1 billion for a site that had revenues of less than $15 million last year (although CEO David Karp has said that figure should hit $100 million this year). That’s an almost bubble-like multiple for a company, and there will likely be plenty of criticism from investors who believe that $1 billion could be better spent elsewhere — in other words, on businesses that would make Yahoo a better return.

But the painful fact is that Yahoo doesn’t just look desperate — in many ways it is desperate. Mayer has made some changes since she took over the ailing former web portal, including the acquisition of Summly and a number of other mobile-focused startups and services, but the company still needs to make some aggressive moves if it is going to jump-start any growth at all. And since Yahoo has about $4 billion in cash on hand, it can arguably afford to make a big bet.

Yahoo buying Tumblr makes sense. Tumblr is only big, cool, newish social platform that Yahoo can afford.—
Henry Blodget (@hblodget) May 17, 2013

For Yahoo, the addition of Tumblr would do a number of things: because of the size and profile of the deal, it would make a major statement about Mayer’s intention to do whatever it takes to revitalize the company, and it would also send a signal to Facebook and Google — and even Apple — that Yahoo is a potential force to be reckoned with when it comes to potential acquisitions. Is doing that worth $1 billion? That’s for Yahoo’s investors and board of directors to decide.

Just as important, it would inject some much-needed life and energy into the somewhat stale lineup of content that the company currently relies on, which caters more to the over-50 set than it does to anyone in the much-desired 18 to 25 demographic. More than any other network, Tumblr is the platform of choice for media-obsessed teens and 20-somethings, who spend massive amounts of time sharing photos and videos and animated GIFs on the site — an engine of potential value that Yahoo desperately needs.

Tumblr gets a massive exit

This doesn’t come without its own risks, of course: As a number of observers have noted, Tumblr’s content contains a large quantity of not only mature or arguably offensive content but outright pornography, which many argue is the source of its massive traffic numbers. How Yahoo (or Facebook for that matter) would deal with this kind of content remains to be seen.

3 q's for Yahoo: 1) Can you convert Tumblr users to Yahoo products? 2) Can you monetize Tumblr PVs? 3) What to do w/ all that Tumblr porn?—
Mark Zohar (@markzohar) May 18, 2013

For Tumblr, meanwhile, being acquired would solve a number of problems — the main one being that the company has gone well beyond the “we’re a startup so we don’t really have to make money” stage, and is facing increasing pressure from the investors who have given CEO David Karp more than $125 million in venture financing, an investment that values the company at about $800 million. Accepting a giant check from Yahoo would take care of that problem in one fell swoop, especially if it was all in cash.

With a major company like Yahoo as a partner, Tumblr could connect its massive audience of users to the firehose of ads and other monetization methods the giant web portal has, and potentially generate much more revenue than it could have by itself. The only lingering question at that point is whether Tumblr fans decide that Yahoo is poisoning the well of social content and community on the site, and decide to flee for greener pastures. In other words, does Yahoo make Tumblr into YouTube — a successful standalone network that can grow and prosper on its own — or does it become MySpace?

The only scenario where a Yahoo-Tumblr combo works is if Yahoo keeps Tumblr separate in the same way Google managed YouTube.—
Mark Birch (@marksbirch) May 18, 2013

Post and thumbnail photos courtesy of Shutterstock / ollyy and Albert Chau

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“We’re thrilled,” Tableau co-founder and CEO Christian Chabot told me during a call after the market closed. One should hope so.

Chabot and his fellow co-founders stand to make a lot of money if today’s closing price holds up, as does its sole investor NEA. The firm put $15 million into Tableau since it launched in 2003, and has rode that sum to profitability and more than $127 million in annual revenue.

Here’s a quick chart (made using Tableau Public) showing who owns how many share and what they’re potentially worth.

The company didn’t really need more capital to operate, Chabot said, but one of the primary drivers was to raise awareness of the company. It has about 12,000 customers, he said, but there are millions more possible users. As part of attracting them, the company is going to expand globally and is working to improve its reach across mobile devices, the cloud and the Mac operating system.

“I don’t believe in the this whole ‘or’ philosophy with computers,” Chabot said. “It’s ‘and’” — meaning people will use desktops and tablets and smartphones.

More prominence and more users singing its praises might also dispel the notion that Tableau is just about visualization. It has some fairly advanced features under the covers (as a commenter to my earlier post about the company’s influence pointed out), even if they’re hidden by the relatively simple user experience.

“Tableau is not a visualization company, per se, it’s really an analytics company,” Chabot said.

However, if the company really wants to expand its reach to everyone one who wants to gain knowledge from data — something Chabot calls a “timeless human need” — it might actually need to get simpler. More marketing can let potential business users know about new features like forecasting and data-extraction, but it won’t make a dentist is Des Moines better at formatting his data.

After raising $254 million in its IPO, though, Tableau is in a good place to do whatever it has to.

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The company’s product, Spire, which provided full SQL support on top of the HBase NoSQL database, was one of the first products to try to blend Hadoop’s scalability with the robustness and familiarity of SQL. That’s now an increasingly crowded space (and has grown since that linked graphic was created). In March, Drawn to Scale expanded its support to MongoDB, as well.

I wasn’t shocked when Stephens told me the news — questions about the four-year-old company’s financial health had been swirling for a while — but to hear of its financial woes was a bit surprising. His account in the post pretty much echoes what I had heard from others:

“It seemed we had everything going for us — paid customers such as American Express, Orange Telecom, Flurry, and 4 others. Our technology worked brilliantly, we had a big hiring pipeline, and we had great media presence against our competitors who raised 10-100x more cash.”

He added:

“Yet five days before we signed term sheets for a big A round or sold the company, we started getting hit by a series of black swans — and we just didn’t have what we needed to recover. I’ll leave the public detail at that level, but I will say that paying employees’ health insurance out of your meager savings is a powerful incentive to change course.”

Up to this point, the company had raised $925,000 from RTP Ventures, IA Ventures and SK Ventures. There’s no word yet on what will come of the company’s intellectual property.

As Stephens — who’s now doing an entrepreneur-in-residence gig at Ping Identity and helping out other startups (including popular wardrobe app Cloth) — succinctly put it during a phone discussion, “We just don’t have the horsepower to keep running the company.”

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Parakweet is a company that’s working to use natural language processing to cull through your tweets and make smart, targeted suggestions based on the data. On Friday, the company plans to announce the launch of two products. One is Bookvi.be, a consumer-oriented book recommendation engine, and TrendFinder For Movies, which is a social media dashboard primarily for entertainment companies to monitor conversations around movies. The latter is a paid product that provides the company with revenue, and the former is free for consumers.

“It’s a very hard problem we’ve tackled, which is accurately identifying sentiments,” CEO Ramesh Haridas said. “With 400 million tweets a day, there are 700,000 a day discussing movies, and if you tried text-matching techniques you’d come back with 40 million results. Many movies and books have very common titles, so you’d just drown in data.”

Both products use natural language processing to figure out how common a title is on Twitter, but also how a consumer is tweeting about a particular product, and they make recommendations based on those tweets. For instance, if I tweeted that a particular book is terrible and no one should ever read it, it would look ridiculous for a book recommendation engine to suggest that book to people. So Bookvi.be is structured to recognize the words I’m using in my tweet and know not to recommend that book. Users can choose to have a weekly email send to them with book suggestions, and they can type in their Twitter username to get book suggestions based on the people they follow.

“The bar on accuracy is very high,” Haridas said. “Especially if it’s sent via email, the precision needs to be intact.”

I’ve looked at a good number of social recommendation tools, and this one definitely stood out. For one, it was incredibly accurate — all the books it suggested were books I would actually read. But most importantly, it didn’t require me to create a new social network, or depend on friends for reviews, so you could get a lot of value from it right away. This is the obvious benefit of using someone else’s social graph, but Twitter seems perfectly suited to making content recommendations for things like books. Because unlike my Facebook friends, the people I follow on Twitter tend to accurately reflect my intellectual interests.

Of course, there are the obvious potential pitfalls of building a product around someone else’s platform, although Haridas said they support Facebook and are adding other platforms. But there’s a good deal of money to be made in accurately processing and understanding the words people are tweeting, as evidenced by Twitter’s acquisition of Lucky Sort this week, a similar company that also tries to figure out what people are talking about on social media.  As I’ve written before, as Twitter ramps up its advertising products it’s more important than ever for the company to be able to provide brands with more accurate ad targeting which hinges on the words people are tweeting and searching.

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Without a doubt, the panel, at Google I/O, was an apt forum for that question. If any company is innovating in a big way, it’s Google, with recent advancements in voice recognition, wearable technology, quantum computing and other realms. So it wouldn’t be surprising to see some of the Google luminaries’ ideas actually come into being. Here’s what they had to say:

“Speech recognition and vision are showing dramatic improvements over the last few years. We just need to scale them up and make them work better. … They’re (mobile devices) going to vanish into much smaller devices that you carry around and aren’t full-size laptops.” — Jeff Dean

“We’re getting more contextualized. The computer is not what you go to to use. It’s something that’s around you all the time and sort of more integrated into your life, rather than a separate thing.” — Peter Norvig

“I would argue that we’re currently living the singularity, where the tool stops and the mind begins will start becoming blurry.” — Thad Starner

So there you have it, folks — the computer as a smaller and more natural extension of the human brain. Now, let’s set the kitchen timer for 10 years and see what actually happens.

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For example, rural Kenyan farmers can easily spend 30 percent of their income on fertilizer, but 80 percent of that fertilizer can be wasted because there is little data collected about the best places and times to use it. The lack of info isn’t just from the farmer’s perspective. Aramburu says when he met the CEO of a major fertilizer company recently, he asked him what he knew about his customers — his response: “very little.”

Re:char CTO Luke Iseman (L) and founder/CEO Jason Aramburu (R) showing off an early prototype of SoilIQ

A soil data cloud in the sky

The two young entrepreneurs latest project emerged from this black hole of information. Working within French telco giant Orange’s first accelerator program, called Orange Fab, Re:char plans to build a $5 plug-in device, called SoilIQ, for an Android phone that can read the moisture levels in soil. During our interview, Iseman takes a very early prototype of the device out of a pouch and shows me how it plugs into the Android phone and taps into the phone’s computing power to detect moisture levels between two screws.

Down the road such a gadget could be developed to pick up other soil data, like fertilizer-level readings. Iseman, an avid gardener, schools me on NPK — nitrogen, phosphorus, and potassium — which he says are the holy grail of fertilizer readings. When SoilIQ is a commercial product, farmers could buy the gadget to take these types of readings, and enhance their farming productivity and the efficiency of their fertilizer use.

But the real value of such a system will likely be in the data collection, and the data analytics and services. The hourly and daily micro readings, which are coded with GPS data, could be used to create a cloud-based location data map of the quality and details of the land. This information could be used to launch data-focused services for both farmers, fertilizers companies, government groups, and others.

Re:char envisions using the data to launch a subscription service for farmers that can alert the farmers to the most fertile land, or even if there’s the danger of crop disease anywhere in the region (maize rot is a huge problem in rural Kenya). Fertilizer companies could use such data to offer better products to farmers, and potentially learn more about the end farmer customer (fertilizer can change hands ten times in Kenya until it reaches the person who will use it, says Aramburu).

Such land data could even be valuable outside of the farming communities. A massive data soil map in the cloud could help determine things like land values, or land ownership issues.

SoilIQ is part internet of things, part sustainability, and part data analytics. The entrepreneurs are actually very focused on developing tools that encourage the more efficient use of resources, which could (and should in my opinion) be the next-generation of cleantech focus.

The core concept also reminds me of what Safecast has been trying to do with its grass roots nuclear radiation sensors and data mapping in Japan. Safecast is bringing that concept to air pollution in L.A., too.

Orange interest

For telco Orange, which has a substantial presence in Kenya, such a system could help them increase mobile data use among customers. The Android devices obviously send the data to the cloud over the cellular networks. And Android phones — as Google announced this week at Google.io — are being used by 900 million people globally.

But potentially even more important is the branding involved. As Orange’s Executive Director of Business Services, Vivek Badrinath, told me in an interview at the Orange Fab event on Monday night, the mobile phone is often times the first branded product that a customer in a developing country has. That brand in turn has a unique ability to transition into offering core services, like mobile banking, and credit.

Orange has a mobile payment system, Orange Money, that is growing nicely and Re:char could plug into it for its planned services. And if SoilIQ becomes a killer app for the bottom of the pyramid, Orange would have a key position in it. Orange is interested enough in Re:char’s new idea that it not only brought the company into its accelerator program, but is investing in its angel round.

Re:char hopes to close an angel round by the end of the program, and later this year raise a series A round. By the end of the three months, Re:char also plans to have its soil moisture-reading gadget developed enough to move it into production.

If you’re a backyard composter or gardener — like Re:charge CTO Iseman — you’re probably wondering if you can get your hands on SoilIQ one day. Aramburu and Iseman tell me that they’d like to make it available in the U.S., too, so the gardening hobbyist could collect their own data.

Clearly, the team is in the very early stages of making SoilIQ, so a lot of their plans will hinge on these next few months. They plan to keep running the kiln bio charcoal business, and think SoilIQ could even help grow that business, too. They also might split the businesses in two down the road.

Ultimately if they can deliver a simple, easy to use, and cheap device, and convince Kenyan farmers to start using it, they could be on their way to unleashing data from Kenya’s rural landscape.

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That’s an apt ticker symbol for a company that is in some ways a bellwether for the current fascination with all things data. Tableau isn’t a big data company, per se, but its visualization software breathes life into many big data calculations. Its focus on making software that’s easy to use and that creates visually captivating charts has turned people from numerous professions into amateur data analysts. (I’ve even used it in the past, including for the first time in 2011.)

Christian Chabot

As Chabot told me during a conversation in 2011, “In any field of human endeavor … there are a hundred to a thousand more people who understand the data of that field more than they understand reporting and analytics.”

Anytime you read about a hot new visualization or analytics startup promising the moon, you’re also seeing the results of what Tableau has sown in terms of the user experience. Many of those same companies will be quick to tell you how limited Tableau’s capabilities are. It’s memory-bound, it doesn’t have a database, it’s not available in the cloud (or on the Mac operating system), it can’t do predictive analytics. All true.

Of course, if it raises the kind of capital it expects to by going public, it can build and buy a lot of those capabilities. If pricing stays flat all day Friday, Tableau stands to make $155 million from its 5 million shares. Previous estimates had Tableau’s market cap at around $1.7 billion at a price of $29 per share (the company’s S-1 filing is available here).

If investors have really bought into the company and the concept of a data-driven world, then who knows. Machine-data expert Splunk wnet public in 2012, flying the big data banner, and saw shares peak at 91 percent above its original asking price of $17.

I’m not suggesting Tableau is the biggest name in data, or even that it will some day become it. This next-generation analytics field is very young, with startups and larger vendors alike sometimes competing against themselves to win wholly new accounts than trying to displace legacy vendors within large enterprises. And every month, it seems, I come across some new startup that was built with the same principles in mind as Tableau, but with the advantage of having today’s best practices baked into its software.

But Tableau definitely commands a lot of the mindshare. How it fares as a public company could be a strong indicator of just how powerful the data movement is, and how well it capitalizes on a new influx of cash will determine how long it stays on the top of customers’ minds.

This post was updated at 7:01 p.m. to include previous estimates of the company’s market capitalization and a link to its S-1 filing.

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Blind trust in black box, or click-and-run, software is a growing problem in science, according to a commentary published Thursday in the journal Science, and the concern extends beyond formal research to other domains that use high performance computing.

The researchers who addressed the “troubling trend in scientific software use” were motivated by a growing unease that the abundance of powerful software is letting scientists derive answers without a thorough understanding of what the software is doing. Software snafus have been responsible for some high-profile data misinterpretations and retractions.

This wouldn’t normally cause a blip on the average citizen’s radar, but now a lot of these scientific conclusions have real-world implications, from climate modeling and weather forecasting to high volume financial trading. In any domain using big data, misplaced trust in the power of software can be problematic, particularly when the decision makers don’t know what the software they are using is doing, said lead author Lucas Joppa of Microsoft Research.

So what does ecology have to do with any of this? Joppa is an ecologist by training, and works on computational techniques in that field that may also have applications for big data more broadly. He and his colleagues surveyed scientists in a sub-field of ecology — species distribution modeling (SDM) — to find out how they choose software and how well they understand its inner workings.

“Lots of SDM techniques are only available as computational methods, but there is a lot of discourse going on in the literature about whether the methods themselves are correct,” said Joppa. Scientists use SDM to forecast where plants and animals will be in the future given current numbers, known habitats, and climate change. It’s a niche area of research, but the disquieting survey results should be noted in any domain where forecasting is done by plugging data into software.

Only 8 percent of the more than 400 scientists who responded had validated their modeling software against other methods. “The number speaks for itself,” said Joppa. “The real crux of the problem is the results from software being published in a peer-reviewed journal, versus the software itself having been peer-reviewed,” which is rare. Software packages, whether proprietary or not, are often black box systems that can’t be opened and inspected. Even if you can get under the proverbial hood, like with open source software, said Joppa, most people will still have no idea what they are looking at, or how to judge its quality.

To top it all off, having confidence in what your software is doing results in a massive computational catch-22: how do you know the software is giving you the right answer, if you can’t get the answer without running the software? The level of confusion over what algorithms are doing in the SDM field is illustrated by a debate over which of two statistical techniques is superior. It turns out, Joppa explained, that the two techniques were mathematically equivalent, but the ways they were implemented in software resulted in big predictive differences.

This sort of mix-up isn’t surprising given the messy nature of software development (if you can even call it that) in research environments. Joppa lauded efforts like Software Carpentry that teach scientists basic software fundamentals for better programming, and said the days of getting a doctorate by merely pushing a button are over.

“Scientists themselves can learn a bare minimum of software engineering,” said Joppa. On the flip side, he said computer science students should have more exposure to scientific methods. “People with traditional software engineering training become uncomfortable with the way scientists want to work with software, where the design and specs are constantly changing. The way that scientific software is built is fundamentally different from consumer apps.”

Developers of scientific software, like MathWorks or SAS, may want to watch this space. If Joppa’s suggestions are implemented, journals may start requiring that even proprietary software be opened up for inspection and peer-review. Nearly half of the surveyed ecologists report using free statistical language R as their primary software, so maybe there is hope yet, both for open, inspectable code, and for computational science becoming more accessible while yielding trustworthy, high impact results.

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Earlier this year Lockheed Martin shared details of its implementation of a D-Wave Systems quantum computer, which reportedly cost $10 million: The contractor is using the computer to develop new aircraft, radar and space systems.

Now Google is taking steps at incorporating more quantum computing into its operations with the Quantum Artificial Intelligence Lab, which will be located at the NASA Ames Research Center in Moffett Field, Calif. Researchers from the Universities Space Research Association will be able to use the machine 20 percent of the time, Forbes reports. That could lead to lots of interdisciplinary thinking and collaboration.

For Google, though, the goal of the initiative is to make strides in machine learning, according to a Thursday Google Research blog post. The best results could trickle down to end users, perhaps in search results and speech-recognition applications.

Quantum computing could mean smarter smartphones

Google has already assembled machine-learning algorithms that involve quantum elements, Hartmut Neven, a Google director of engineering, explained in the post:

One produces very compact, efficient recognizers — very useful when you’re short on power, as on a mobile device. Another can handle highly polluted training data, where a high percentage of the examples are mislabeled, as they often are in the real world.

It’s not hard to imagine how quantum computing could inform machine learning on a smartphone with just a drop of battery life left. It could be that a smarter smartphone one day will take a minuscule amount of input and determine with a high probability who a user wants to talk to or what information it needs right away, rather than forcing the user to cycle through a string of commands and risking the death of the battery altogether.

The applications might have arisen after Google’s earlier partnership with D-Wave, which came to light in a different blog post from Neven in 2009.

Google has already used machine learning to recognize faces and other things in photos and videos. New technology Google executives talked about at the Google I/O developer conference in San Francisco on Wednesday also appears to use machine learning to stitch together photos and clean them up.

What Google has learned so far is the best results come from blending regular binary computing using ones and zeros with quantum style computing. Quantum computing accommodates the space between a one and a zero with quantum bits of information, or qubits. It can express likelihood as well as take shortcuts by approximating when handling certain kinds of workloads. Given what Google has observed thus far, it could decide to build hardware combining quantum and classical computing capabilities.

For now, though, Google is diving deeper into quantum computing with the D-Wave machine. The move could kick off a sort of arms race for webscale companies to buy quantum computers and come up with new notions by way of probabilistic logic. In this way, Google could help push the development of quantum computing much like its invention of MapReduce changed the way firms do distributed data processing.

In any case, quantum computing has a long way to go before reaching commercial viability. That could take decades (so far it has). But because the organization at the helm of the quantum research is Google and not IBM or Bell Labs, regular people could start seeing much more of the advantages in just a few years’ time, which in turn could drive commercialization.

Feature image courtesy of Shutterstock user pixeldreams.eu.

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In traditional analytics, manual pattern evaluation is the weak link that slows speed-to-insight and risks overlooking key insights. In an hour, humans can only explore dozens out of the millions of potential patterns hidden in the data. In minutes, BeyondCore explores those millions of possibilities, conducts rigorous statistical tests and presents the most important insights, without the risk of human bias or error. Users then examine the short list to further refine the most relevant insights. Competitors who focus on building faster and more powerful tools for human experts to ask specific questions of their data are simply missing the point.

BeyondCore’s automated algorithms test, rank and present the very best patterns your data has to offer. There is a fundamental difference between merely answering your questions and discovering questions with useful answers. BeyondCore identifies the best questions to ask.

Amazon CTO Werner Vogels and CEO Jeff Bezos on stage at AWS: Reinvent

Here are a few things AWS (which after all, remains the cloud to beat) could do to shore up its defenses as GCE, Windows Azure and soon VMware’s AWS competitor (to be re-announced May 21) come online.

1: Get more granular in pricing

One headline item Wednesday was Google’s decision to rent cloud instances by the minute instead of by the hour (well, you have to buy a minimum of 10 minutes with incremental charges for each additional minute.) AWS rents by the hour, which is something it could well change. Both companies are late to this particular feature however: Both Cloud Sigma and Profitbricks have offered sub-hour models for some time.

2: Keep pounding on enterprise support …

And management options like Trusted Advisor, which instructs AWS users on how to deploy their workloads more efficiently and more securely.  The knock on Google remains that it (let alone its cloud) doesn’t “get” the enterprise — millions of  Google Apps and Gmail business users notwithstanding. A CIO might ask herself: “Gee, do I want to trust my workloads to a search and advertising company? I still can’t believe I’m trusting some of them to a book seller! “

If enterprise is a key business, you have to keep earning it.

3: Prove that AWS is an Amazon corporate priority

The perception that Amazon.com, Jeff Bezos and corporate don’t care that much about AWS continues to dog the cloud services arm. It was a big deal that Bezos showed up at AWS: Reinvent last year, but he really doesn’t talk about the cloud business all that much. What might help there? BREAKING OUT AWS REVENUE! If AWS is a $2 billion-a-year-plus business, get transparent about it. And talk profitability, not just revenue. Come on guys, it’s time.

4: Keep the services coming

Much was made of Google’s brand new NoSQL database service, which, as my colleague Derrick Harris pointed out, is “eerily similar” to Amazon’s DynamoDB. Google SVP Urs Hölzle noted that Google, 11 months after announcing GCE, rolled out 10TB persistent disk, something that an “unnamed competitor” hadn’t done in its 7 years. That may be, but AWS has lots of other services and perks and maturity counts — especially among corporate buyers.

So, Amazon. It’s your move.

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Yet Vigano, who is the CEO of Heapsylon, a startup based out of Redmond, Wash. and wears Armani shirts (casual Armani, but still Armani), thinks he has a solution to wearable computing that people will find, well … wearable.

At first glance Heapslylon makes a pair of smart socks that mimic many of the data gathering functions of any other fitness tracker on the market. Except that they are socks. And it’s tough to imagine one wearing the same pair of sensor-packed socks each day or even wearing socks every day. How is this a good idea? How is this the future of fashion and technology?

The Sensoria smart sock and anklet.

But just as the Kindle Fire is merely a vehicle for Amazon’s real ambitions, as opposed to the sum of them, Heapsylon isn’t really in the sock business. The socks are made of a special material that the company developed — it is reportedly comfortable, washable and packed with sensors. And that material is the crux of what Heapslyson has developed. The socks are both an effort to put the new material through its paces with the hardest-working piece of clothing in a person’s wardrobe, as well as an application to showcase what the material can do.

Heapsylon was started in October 2010 by three former Microsoft employees: Davide Vigano, Mario Esposito, who is the CTO at Heapsylon, and Maurizio Macagno, the VP of development at the company. Both Macagno and Esposito worked on the Kinect, but all three left Microsoft to pursue this vision of combining technology and fashion in a way that could advance both industries.

“The outfit is the computer,” said Vigano of their vision. But after they quit, and started playing around with tiny sensors and conductive fabrics they realized that there was a gap between what they wanted and what technology could provide. So they set about researching materials. The result of that is the fabric that they have since turned into socks.

“Quite frankly we thought it would easier,” said Vigano. “The materials research has been challenging to say the least.”

The anklet for the Sensoria smart sock.

The sock and they accompanying hardware that tracks the data from the sock’s sensors are called the Sensoria Fitness smart socks. There will be two or maybe three pressure sensors in the sock that will share exact data on how many steps a person takes, their stride, whether they tend to turn in or out when they run, and maybe even weight change. The socks come with an ankle bracelet that gathers the data from the sock and transmits it (see above.) It will eventually be possible to use the socks as a scale to track your weight once Heaspylon gets the algorithms down.

Vigano said the company plans to launch the package of the socks and the hardware via a crowd-funding platform like Kickstarter or Indiegogo within the next month or two. While at first it will be targeted at the hard-core running market, the socks might find a home in other sports such as golf, where tracking the weight on the ball or heel of the foot can help improve performance. The price for the package is yet to be determined. And he declined to share the cost of the sensor-rich fabric.

But perhaps most important for the future of Heapsylon, and the future of fashion-forward wearables, is that Vigano aims to license the fabric technology and the data it gathers from its socks for other uses. For example, the material could be used in football helmets to measure the incidence of concussions, or the exact footfall data from the sock might supplant the general data provided by a pedometer for a person’s run. And other companies might use the socks to develop data profiles around other sports, like the golf example above.

The Heapsylon employees and founders.

There’s also no reason to stick only with pressure sensors. Other sensors could be embedded into the Heapsylon fabric, although the company hasn’t focused on that as a small startup with a relatively small and undisclosed amount of angel funding. Vigano hopes to seek a formal venture round after the crowd-funding campaign proves (or disproves) the market opportunity.

For now, the five-person startup is heading toward a production version of the Sensoria Sock hoping that it will be the launch of a revolution in wearable computing that goes far beyond the smart watches and accessories available today. If Heapsylon’s vision of making technology disappear into fabric gains ground, then perhaps Vigano’s future Armani shirts might not just look good, but they could do good by sharing relevant data that monitors his health or his whereabouts for loved ones.

Of course, such data could also be used for less-then-noble causes, or the market may never buy into the idea of smart socks. But as someone who looks at the intersection between technology and fashion, I welcome anyone who wants to develop a product that emphasizes the form, the function and the feel of the wearable tech as opposed to just the tech.

Not everyone wants to look like they just got off the Caltrain at Mountain View.

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It remains to be seen whether Google really manages to pull this off in an uncluttered way, particularly on smartphones, but the changes do look promising. However, Google’s mobile maps do present one problem, particularly for those travelling in foreign climes, and that doesn’t look set to change anytime soon: they only offer limited offline functionality. Maps for specific places can be easily pre-cached for offline use, but you won’t get search or routing functionality without a data connection.

Enter Skobbler with its new Android app, GPS Navigation & Maps. A couple of weeks ago, the Berlin-based startup heavily revamped its iOS ForeverMap app to bring it in line with the more advanced Android version of the same, and now it’s moved the Android app on significantly – so significantly that it’s even renamed it.

All in one

To be precise, Skobbler’s new Android app combines two previous apps, one of which was for maps (ForeverMap 2, whose users will get this upgrade for free) and the other (Skobbler Navigation, shut down a year back) for turn-by-turn navigation. The company claims, rightly I believe, that the result is the only Android app to combine both these functionalities for both online and offline use. What’s more, open-stuff fans can revel in the fact that GPS Navigation & Maps is based on the crowdsourced and highly accurate OpenStreetMap project.

The fact that the app is priced at just $1 shouldn’t hurt either, although buyers should be aware that this comes with only one free downloadable country for offline use. Beyond that, cities will cost $0.99, states $1.11, countries $2.22, continents $4.44 and the whole world $7.77 – not only is this way cheaper than the likes of TomTom Navigation or CoPilot Live, but it also provides the opportunity to download specific areas rather than entire countries or continents: a useful option if you’re concerned about storage.

A separate free version gives you full online maps for the world, along with a 14-day trial of the turn-by-turn, voice-aided navigation functionality.

Skobbler’s iOS maps app allows you to download any country for offline use for free, but turn-by-turn functionality comes in a separate app on that platform. Like that app, though, GPS Navigation & Maps acts as a showcase for Skobbler’s zippy NGx map engine – the company is keen on selling its technology to partners, particularly those in the automotive industry.

Anti-Google opportunity?

Skobbler’s app has clear appeal for those travelling abroad – data roaming charges are still eye-bleedingly high in most cases – but what about customers who are just looking to use it locally?

There, the company may find a willing audience in the shape of the anti-Google resistance. One peculiarity of Android is that, being Linux-based and ubiquitous, it’s the platform of choice for the open-source crowd while also providing a growing privacy threat, of the kind that horrifies the same people.

The mapping updates that Google announced on Wednesday are clearly designed to make Google Maps more of a personalized interface for everyday movements. This should manifest itself in a particularly tightly integrated way on Android, as the lines blur between Maps and Now and everything else Google is baking into the same pie. A lot of people won’t like that.

It’s unlikely that anyone will completely replace Google Maps for Android with a service such as Skobbler’s, as Google’s in-house location infrastructure is probably too baked-in these days to avoid. But, in terms of consciously firing up a mapping app to negotiate the world around them, some people may find value in choosing a non-Google option, so that their often highly personal location searches don’t get fed into the Great Google Data Stew. For those people, GPS Navigation & Maps could provide a tantalizing alternative.

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AWS has a managed NoSQL database service called DynamoDB that’s replicated across three availability zones to ensure its stays up. Google’s Cloud Datastore sounds eerily similar, according to the product’s website (although Google calls its product “NoSQL-like). It’s fully managed, built for speed and scale and is replicated across data centers. For some queries, Google even promises that Cloud Datastore will support ACID transactions.

Although the services advertise similar features in terms of availability and scalability, they’re quite different technically. Cloud Datastore is based on Google’s BigTable database (and a library called Megastore on top of it) while DynamoDB is based on Amazon’s Dynamo database. You can get details on Datastore  and how it works here. Pricing information is available here.

If its goal is to compete with AWS, though, Google’s cloud platform still has a long way to go. Yes, it has most of the key services in place and even some seeming advantages in certain areas, but it’s lacking the incredible breadth of services AWS offers — everything from virtual server instances to a devops service to a hosted data warehouse. It’s also lacking a seven-year reputation for being an all-around reliable platform and an ever-growing list of large-enterprise users.

Of course, there’s also an argument to be made that Google doesn’t really have to compete with AWS at all when it comes to cloud computing. AWS made a name for itself by  taking all the new workloads from startups and corporate developers who wanted to build new types of applications and didn’t want to deal with the IT department; Google has the same opportunity ahead of it. New programming languages like Go and the unique nature of the rest of Google’s services, Cloud Datastore included, could make it the go-to place for a class of developers that likes to push the envelope in terms of application design.

Oh, and Google has a little ace up its sleeve called Android. If someone is so inclined to develop mobile applications for the most-popular mobile operating system on the planet, there are worse places to host them.

This post was updated at 5:35 p.m. to clarify that DynamoDB and Cloud Datastore are based on different underlying technologies.

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Koruta, which I profiled last November, makes a photonics chip that allows signals to pass between chips using light (photons) instead of electrons. This makes communications between chips faster, something becoming more important inside the data center as networks become flatter, faster and fatter.

The Kotura chip is a fiber-based transceiver that can deliver 100 gigabits per second inside the data center. The transceiver could live on a board next to the CPU or inside a switch and could eventually expand to deliver a terabit per second (Tbps). Currently it’s used in high-performance compute clusters, which are also the most-popular home for Infiniband. From my post in November:

While one 1 Tbps is crazy fast when you consider that many data centers are currently upgrading to 10 gigabit Ethernet between servers, it’s going to be necessary. Arlon Martin, VP of Marketing, Government Contracts & Industry Relations at Kotura, tells me that customers are building products for the high-performance computing sectors but also for real-time data processing. The goal is bringing a low-power and less expensive optical part into a rack of servers, able to scale up to terabit per second capacities.

Kotura isn’t the only company trying to bring fiber optics into the data center. Plexxi is building fiber-based switches, while Facebook and the Open Compute Project in January announced their own plans to integrate photonics into their open hardware program. Intel, Cisco and IBM all have research or have acquired startups in this space as well. With its emphasis on fast networking, it makes sense for Mellanox to follow suit.

The company plans to continue offering the Kotura transceiver and open a research center in Monterrey Park, Calif., where Kotura is headquartered. From the Mellanox release:

“Operating networks at 100 Gigabit per second rates and higher requires careful integration between all parts of the network. We believe that silicon photonics is an important component in the development of 100 Gigabit InfiniBand and Ethernet solutions, and that owning and controlling the technology will allow us to develop the best, most reliable solution for our customers,” said Eyal Waldman, president, CEO and chairman of Mellanox Technologies.

At the time of my profile, Kotura had raised undisclosed millions from ARCH Venture Partners, Fuse Capital, GF Private Equity and others. It has an established customer base in the telecommunications business where it has sold product since 2006. But last year it began targeting the data center, where it apparently attracted Mellanox’s eye.

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