Zoomdata Founder and CEO Justin Langseth started Zoomdata on the premise of building a company that envisioned BI free from decades of legacy baggage. In 2012, that means abandoning the desktop and designing for tablets, and taking advantage of the nearly unlimited computing power available in the cloud and even on our mobile devices. It also means designing a user experience so intuitive that users know how it works without ever really having to learn.

Just like someone can open up Google Earth and know they’re seeing the planet Earth, Langseth said Zoomdata users should open the app and say, “Oh, that’s my business.” And then they should be able to easily zoom in right where they want to go, using only their fingers. In a few swipes and pinches, Langseth said, users are soon uttering the business equivalent of “that’ my house, that’s my car, that’s my tree.”

It’s a pretty heady concept for a guy like Langseth who has been entrenched in the space for years, first at MicroStrategy in the 1990s and most recently doing a text-analysis startup, but he appears to have pulled it off thanks to the array of powerful components now floating around the web for free. Zoomdata is able to take data from anywhere — web apps, enterprise systems, Hadoop, email, you name it — and process it as it hits the system using an open source stream-processing engine called Storm. Once it’s processed, Zoomdata applies intelligence to figure out the best way to display that data visually and puts the result on the screen.

Data is like paint on a palette

That, Langseth said, is where the magic really comes in. “We’ve been thinking of data as kind of like paint,” he explained. The app takes many of the concepts from the D3.js project for creating HTML documents using data, but then makes them interactive and “lights them up with real-time data.”

The human interaction becomes a combination of watching a movie and finger painting. Combining data sources and sets by swiping your fingers is akin to blending colors from a palette. The interface comes with set of buttons for pausing, rewinding and fast-forwarding the visualization, too — because it’s a real-time engine, the data keeps coming and the visualization keeps changing until someone temporarily stops the flow.

Under the hood, of course, Zoomdata is a lot more complex than meets the eye. It’s all about scale, speed and huge amounts of data, Langseth explained.  The backend does all the work and only streams the data required at any given time, so as to save the processing load on the user’s device. If a user presses pause or rewinds, the system keeps processing new data while also letting the user interact with the older data unaffected. Zoomdata also supports historical data sitting inside databases and other data stores so that users can compare their real-time information against the past.

The future: Bigger screens, smarter visualization and stiff competition

As if all this doesn’t sound futuristic enough, Langseth’s plans to take the technology further. “There’s a whole bunch more intelligence we can add to the system,” he explained, referencing his plans to incorporate machine-learning algorithms that will make the system even better at choosing how to visualize the raw, often schemaless data it’s receiving.

He also likes the idea of big touch screens, like CNN Big Board big. Sometimes when he has his iPad display showing on this 50-inch office television to play music, people come in and just assume they can start interacting with it like a touch screen. “Not just being able to see it, but to touch it, really excites people,” Langseth said.

Of course, as with all companies trying to carve out their space in the lucrative BI market — including the newly cash-rich Platfora — Zoomdata will have to prove itself a worthwhile alternative to big, expensive legacy technologies. Langseth thinks the real-time, mobile nature of his company’s product will at least make it a nice complement to existing desktop-based BI tools for historical data. And like pretty much everything powered by the cloud and rendered on a mobile device, its simplicity might appeal to a lot of users who don’t need the price or complexity that comes along with much legacy software.

“Some people need 10,000 features,” he said, “but most people need 5 features.”

We’ll see if Langseth is right soon enough. Zoomdata is currently in private beta after development began in March, and the company hopes to keep refining the user experience and open it up for broader consumption next year.

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Every venture capital firm has made some preliminary investments in big data companies, he said, but those companies will likely need some recognizable adoption or income in order to justify further funding on desirable terms. Even for a team of veteran entrepreneurs and technologists like Nodeable, which had built a platform that served as a front-end complement to Hadoop by analyzing streaming data, it can be challenging to get over that hump. We’ve written about this in our coverage of how challenging it can be for companies to get their Series B round of funding without having transcended to rockstar status.

Dave Rosenberg (left) speaking at Structure 2012.
(c)2012 Pinar Ozger pinar@pinarozger.com

Despite not making it past the first round, Rosenberg has learned a lot. Here’s what he thinks he’s learned about how the big data market is shaping up and pitfalls others startups should try to avoid.

1. If you’re doing big data, do it open source and on-premise.

Nodeable thought it was getting ahead of the curve by launching its stream-processing platform as a cloud service, but it probably was too far ahead. With rare exception, Rosenberg said, “Organic growth in big data right now is clearly with open source projects that are behind the firewall.”

Everyone agrees in theory that delivering big data tools as cloud services is the right way to go (myself included, for what it’s worth), but most companies aren’t there yet. Even with Amazon’s popular Elastic MapReduce service, he added, people are running a lot of jobs but not necessarily only exposing small amounts of their overall data sets to the cloud and then are pulling the results right back behind the firewall.

“From a user perspective,” Rosenberg said, “they want to download things.” That’s why, he added, “Even though Amazon [Web Services] is booming, it’s still websites.”

So, rather than hire an enterprise sales team to start pushing its product, Nodeable’s next move if it didn’t sell was to open source its technology so the large companies that expressed interest could begin experimenting with it on their own servers. That wouldn’t have guaranteed success, but it would have aligned with companies’ realities about where their data is stored and who has access to it.

2. Hadoop is not a license to print money.

Although some companies are making a lot of money selling Hadoop product, simply incorporating the technology into a product does not guarantee success. “Cracking into the Hadoop space is very difficult,” Rosenberg said.

Nodeable, for example, had close relationships with key members of the Hadoop community — including Cloudera CEO Mike Olson, who sat on the company’s board — anf it still wasn’t able to tap into that market like it had planned. Part of that ties into the previous point about being an on-premise solution, but another part has to do with technology choices that align with where others are pushing the platform. Whereas Nodeable relied heavily on Storm for real-time processing, the Hadoop platform vendors such as Cloudera and Hortonworks are putting their real-time energy behind HBase at the database layer.

3. Life is easier as an app.

In some ways, Rosenberg said, companies that sell applications might have an easier time succeeding time than those that sell infrastructure. The former come with their own use cases, while the latter are always searching for new use cases that let them find their way into new markets. In cloud computing, especially — where Rosenberg says people often expect free software — it’s probably easier to get them to pay for applications than for big data infrastructure. (Nodeable, I would say, fell somewhere in the middle.)

And when acquisition time comes, cloud companies such as Salesforce.com and Workday that have to make big data plays will probably be looking for applications rather than infrastructure tools, Rosenberg said. (Workday, for what it’s worth, just partnered with Hadoop BI startup Datameer on this front.) If you’re looking for the best-possible situation and don’t want to end up part of a company that will take your talent and bury your technology because it thinks it can build something better, having an army of loyal users is good leverage.

4. If you must sell, choose wisely.

Appcelerator wasn’t its only suitor when Nodeable decided to sell, Rosenberg said, but it was the best one. Rather than being an “acqui-hire” situation, where the hard work the team put into Nodeable’s StreamReduce technology would have been lost, the technology exists and will help build a real-time analytics engine into Appcelerator’s mobile-app development service. One reason he’s so confident about the decision is that Nodeable already knows the Appcelerator team and its plans — the two companies had been working together for months to improve Appcelerator’s infrastructure.

Feature image courtesy of Shutterstock user Rashevskyi Viacheslav.

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Fortunately, New Yorkers are looking out for each other as they always do. Outside bars and bodegas, merchants with generators are sharing them with residents who need to charge up and contact the outside world. This was the scene near Avenue A in the East Village where Percy’s Tavern set up tables with dozens of power outlets to use free of charge:

Similar scenes played out in front of hairdressers and coffee shops around the blacked out part of the city. Strangely, for this week at least, the cell phone habits of many New Yorkers resemble those of rural Africans who regularly pay small sums to charge their mobile devices from a generator.

The situation in Manhattan also highlights how, in a crisis, the city’s post-industrial economy still ultimately depends on very industrial fuels like diesel. (To see how we might one day progress beyond this, see the excellent reporting by Katie Fehrenbacher earlier this week.)

Finally, this scene on 2nd Avenue shows that water is also an issue for some New Yorkers in the black out zone:

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To understand how StreamReduce works, it’s helpful to take a look back at how it came to be. As it turns out, Nodeable Founder and CEO Dave Rosenberg told me, the company realized quickly it needed to do something different if it wanted to add value in the systems management space, and that something was analytics. Rather than just produce a stream of tweet-like alerts to sysadmins, Nodeable would actually alert them to anomalies and emerging patterns that might signify a bigger problem to come. In doing that, Rosenberg said, the company realized it had actually created a real-time complement for Hadoop.

Fed up with systems management (it’s hard to do a repeatable cloud service in that space, and no one wants to pay for systems management, Rosenberg said), Nodeable, with support from its customers and investors, decided StreamReduce was its real business. “Between Cloudera [whose CEO Mike Olson is on Nodeable's board] and Hortonworks, it took us five muntes to find more customers willing to pay for this than we we thought we could find managing AWS,” Rosenberg said.

It works similarly to the original Nodeable product — and the UI will be familiar to legacy users — but the use cases are as broad as customers imaginations. Clickstream analysis, systems monitoring, fraud detection, you name it. Essentially, users define the metrics they want to monitor, everything hits StreamReduce as a JSON file, and the system analyzes it and delivers alerts around counts, patterns and anomalies in real time. Once that’s done, it can feed data into Hadoop for more in-depth batch analysis later on.

One beta user that happens to be a major web retailer has been using StreamReduce to try and determine why customers are abandoning their online shopping carts. It’s tactic was to analyze shopping cart abandonment against slow-loading product images from Amazon S3 and negative comments on Twitter and try to determine any correlations. Doing all this after the fact using Hadoop only wouldn’t be much use as the problems were occurring.

For the web-stack aficionados out there, StreamReduce runs in the Amazon Web Services cloud, using a collection of AWS tools, as well as MongoDB and Amazon’s DynamoDB. Although, Rosenberg said, MongoDB — the most-mature NoSQL option at the time Nodeable started building — might get swapped with Cassandra later this year. For Nodeable’s high-scale use case, MongoDB just isn’t the right fit.

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It’s therefore far from controversial to say that Hadoop is firmly planted in the enterprise as the big data standard and will likely remain firmly entrenched for at least another decade. But, building on some previous discussion, I’m going to go out on a limb and ask, “Is the enterprise buying into a technology whose best day has already passed?”

First, there were Google File System and Google MapReduce

To study this question we need to return to Hadoop’s inspiration – Google’s MapReduce. Confronted with a data explosion, Google engineers Jeff Dean and Sanjay Ghemawat architected (and published!) two seminal systems: the Google File System (GFS) and Google MapReduce (GMR). The former was a brilliantly pragmatic solution to exabyte-scale data management using commodity hardware. The latter was an equally brilliant implementation of a long-standing design pattern applied to massively parallel processing of said data on said commodity machines.

GMR’s brilliance was to make big data processing approachable to Google’s typical user/developer and to make it fast and fault tolerant. Simply put, it boiled data processing at scale down to the bare essentials and took care of everything else. GFS and GMR became the core of the processing engine used to crawl, analyze, and rank web pages into the giant inverted index that we all use daily at google.com. This was clearly a major advantage for Google.

Enter reverse engineering in the open source world, and, voila, Apache Hadoop — comprised of the Hadoop Distributed File System and Hadoop MapReduce — was born in the image of GFS and GMR. Yes, Hadoop is developing into an ecosystem of projects that touch nearly all parts of data management and processing. But, at its core, it is a MapReduce system. Your code is turned into map and reduce jobs, and Hadoop runs those jobs for you.

Then Google evolved. Can Hadoop catch up?

Most interesting to me, however, is that GMR no longer holds such prominence in the Google stack. Just as the enterprise is locking into MapReduce, Google seems to be moving past it. In fact, many of the technologies I’m going to discuss below aren’t even new; they date back the second half of the last decade, mere years after the seminal GMR paper was in print.

Here are technologies that I hope will ultimately seed the post-Hadoop era. While many Apache projects and commercial Hadoop distributions are actively trying to address some of the issues below via technologies and features such as HBase, Hive and Next-Generation MapReduce (aka YARN), it is my opinion that it will require new, non-MapReduce-based architectures that leverage the Hadoop core (HDFS and Zookeeper) to truly compete with Google’s technology. (A more technical exposition with published benchmarks is available at http://www.slideshare.net/mlmilleratmit/gluecon-miller-horizon.)

Percolator for incremental indexing and analysis of frequently changing datasets. Hadoop is a big machine. Once you get it up to speed it’s great at crunching your data. Get the disks spinning forward as fast as you can. However, each time you want to analyze the data (say after adding, modifying or deleting data) you have to stream over the entire dataset. If your dataset is always growing, this means your analysis time also grows without bound.

So, how does Google manage to make its search results increasingly real-time? By displacing GMR in favor of an incremental processing engine called Percolator. By dealing only with new, modified, or deleted documents and using secondary indices to efficiently catalog and query the resulting output, Google was able to dramatically decrease the time to value. As the authors of the Percolator paper write, ”[C]onverting the indexing system to an incremental system … reduced the average document processing latency by a factor of 100.” This means that new content on the Web could be indexed 100 times faster than possible using the MapReduce system!

Coming from the Large Hadron Collider (an ever-growing big data corpus), this topic is near and dear to my heart. Some datasets simply never stop growing. It is why we baked a similar approach deep into the Cloudant data layer service, it is why trigger-based processing is now available in HBase, and it is a primary reason that Twitter Storm is gaining momentum for real-time processing of stream data.

Dremel for ad hoc analytics. Google and the Hadoop ecosystem worked very hard to make MapReduce an approachable tool for ad hoc analyses. From Sawzall through Pig and Hive, many interface layers have been built. Yet, for all of the SQL-like familiarity, they ignore one fundamental reality – MapReduce (and thereby Hadoop) is purpose-built for organized data processing (jobs). It is baked from the core for workflows, not ad hoc exploration.

In stark contrast, many BI/analytics queries are fundamentally ad hoc, interactive, low-latency analyses. Not only is writing map and reduce workflows prohibitive for many analysts, but waiting minutes for jobs to start and hours for workflows to complete is not conducive to the interactive experience. Therefore, Google invented Dremel (now exposed as the BigQuery product) as a purpose-built tool to allow analysts to scan over petabytes of data in seconds to answer ad hoc queries and, presumably, power compelling visualizations.

Google BigQuery

Google’s Dremel paper says it is “capable of running aggregation queries over trillions of rows in seconds,” and the same paper notes that running identical queries in standard MapReduce is approximately 100 times slower than in Dremel. Most impressive, however, is real world data from production systems at Google, where the vast majority of Dremel queries complete in less than 10 seconds, a time well below the typical latencies of even beginning execution of a MapReduce workflow and its associated jobs.

Interestingly, I’m not aware of any compelling open source alternatives to Dremel at the time of this writing and consider this a fantastic BI/analytics opportunity.

Pregel for analyzing graph data. Google MapReduce was purpose-built for crawling and analyzing the world’s largest graph data structure – the internet. However, certain core assumptions of MapReduce are at fundamental odds with analyzing networks of people, telecommunications equipment, documents and other graph data structures. For example, calculation of the single-source shortest path (SSSP) through a graph requires copying the graph forward to future MapReduce passes, an amazingly inefficient approach and simply untenable at scale.

Therefore, Google built Pregel, a large bulk synchronous processing application for petabyte -scale graph processing on distributed commodity machines. The results are impressive. In contrast to Hadoop, which often causes exponential data amplification in graph processing, Pregel is able to naturally and efficiently execute graph algorithms such as SSSP or PageRank in dramatically shorter time and with significantly less complicated code. Most stunning is the published data demonstrating processing on billions of nodes with trillions of edges in mere minutes, with a near linear scaling of execution time with graph size.

At the time of writing, the only viable option in the open source world is Giraph, an early Apache incubator project that leverages HDFS and Zookeeper. There’s another project called Golden Orb available on GitHub.

In summary, Hadoop is an incredible tool for large-scale data processing on clusters of commodity hardware. But if you’re trying to process dynamic data sets, ad-hoc analytics or graph data structures, Google’s own actions clearly demonstrate better alternatives to the MapReduce paradigm. Percolator, Dremel and Pregel make an impressive trio and comprise the new canon of big data. I would be shocked if they don’t have a similar impact on IT as Google’s original big three of GFS, GMR, and BigTable have had.

Mike Miller (@mlmilleratmit) is chief scientist and co-founder at Cloudant, and Affiliate Professor of Particle Physics at University of Washington.

Feature image courtesy of Shutterstock user Jason Prince; evolution of the wheel image courtesy of Shutterstock user James Steidl.

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The question is — does the world need another real-time data processing tool? After all there are many tools like HStreaming (using Hadoop), the open source S4 and StreamBase, but the overall analytics market (if you can call it a market) is already fragmented. The Storm code comes from Twitter’s acquisition of BackType last month and seems to be an effort to get folks comfortable parsing data on Twitter.

The post does an excellent job laying out use cases for Storm and hints at more to come. While the code can deal with distributed nodes and huge amounts of data a la Hadoop or Map Reduce, Storm handles jobs that are “infinite.” It’s not for a data processing job with an end point, it’s good for streams of data and continual processing. From the post by Nathan Marz:

Here’s a recap of the three broad use cases for Storm:

• Stream processing: Storm can be used to process a stream of new data and update databases in realtime. Unlike the standard approach of doing stream processing with a network of queues and workers, Storm is fault-tolerant and scalable.
• Continuous computation: Storm can do a continuous query and stream the results to clients in realtime. An example is streaming trending topics on Twitter into browsers. The browsers will have a realtime view on what the trending topics are as they happen.
• Distributed RPC: Storm can be used to parallelize an intense query on the fly. The idea is that your Storm topology is a distributed function that waits for invocation messages. When it receives an invocation, it computes the query and sends back the results. Examples of Distributed RPC are parallelizing search queries or doing set operations on large numbers of large sets.

But wait! There’s more! At the end of the post we are assured that there’s more to Storm than the blog post has even defined, which we can learn more about next month at the Strange Loop event. From the post:

I’ve only scratched the surface on Storm. The “stream” concept at the core of Storm can be taken so much further than what I’ve shown here — I didn’t talk about things like multi-streams, implicit streams, or direct groupings. I showed two of Storm’s main abstractions, spouts and bolts, but I didn’t talk about Storm’s third, and possibly most powerful abstraction, the “state spout”. I didn’t show how you do distributed RPC over Storm, and I didn’t discuss Storm’s awesome automated deploy that lets you create a Storm cluster on EC2 with just the click of a button.

So for those anxious to test out a new method of crunching terabytes of real-time data on the fly, get thee to GitHub! And wait.

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RIM’s workforce has ballooned since 2006, indicating on the surface that it’s keeping pace with the fast-growing mobile-device segment. Unfortunately, sales of BlackBerry phones aren’t. Companies such as Apple, Samsung and even beleaguered Nokia all sell more smartphones than RIM, and the company only makes smartphones — with the exception of the new PlayBook tablet, which has some pros but more cons to some and doesn’t appear to be selling well. Here is the company’s positioning on the job cuts:

RIM today provided further details on its cost optimization program, which is focused on eliminating redundancies and reallocating resources to focus on areas that offer the highest growth opportunities and alignment with RIM’s strategic objectives.  The workforce reduction is believed to be a prudent and necessary step for the long term success of the company and it follows an extended period of rapid growth within the company whereby the workforce had nearly quadrupled in the last five years alone.

The entire situation reemphasizes that RIM has been too slow to change in a market that’s moving fast. The BlackBerry Storm, an attempt at an all-touchscreen device, was met with fanfare in 2008, but it never materialized as a solid competitor to Apple’s iPhone. Last year’s BlackBerry Torch was more evolution than revolution.

And the company’s plan to run future phones on a QNX-powered platform makes sense, but RIM bought QNX in April of 2010 and there are still no handsets announced for the new operating system. Instead, new Bold handsets are the latest offerings announced; they appear delayed and will run a new version of BlackBerry OS, not QNX. They’re also not expected to be upgradable to QNX either.

It wasn’t that long ago when the words “smartphone” and “BlackBerry” were synonymous to many. The times have changed drastically, but RIM has only changed marginally.

Image courtesy of Flickr user Robert_Elder

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But let’s face it, we could still get more nasty weather before the winter is over. Personally, I think inclement weather means that it’s time to stay home, stay warm and take time off with family. But if you must work, then you might as well be prepared. I’ve collected some resources that may be helpful to those of us who work remotely.

Power

A couple of my clients have been affected by the unusually cold weather causing power outages in places like Texas.

If you’re using a desktop, you’ll want to get an uninterruptable power supply (UPS). But don’t expect too much. Reasonably-priced UPSes are intended to give you enough time to save your work and shut down gracefully. They won’t let you continue working for a long time; the ones I’ve tried don’t last more than an hour or so.

Of course, you can use a laptop, and rely on its battery for however long it will last. And you might be able to use backup power sources like the ZAGGSparq for smaller devices.

Internet connection

Even if your power stays on, your Internet connection might not. My primary Internet service comes from a cable modem, but if it goes down, it’s nice to be able to tether my smartphone to my laptop, use a mobile hotspot, or use a phone with hotspot capability built in.

If you don’t want to spend the extra money for the above on a regular basis, and you live in an area with 4G coverage, you might want to invest in the Rover Puck from Clear. This device, which I’ve written about previously, is reasonably cheap and lets you buy 4G connectivity on a pay-as-you-go basis.

Communicate

Assuming you have power and an Internet connection, you can use the communication and conferencing options that Simon collected last spring. Also, check out this updated list of web conferencing alternatives.

Of course, you’ll want to let clients and colleagues know what you are doing, something that’s even easier now then when I last wrote about the subject a couple of years ago.

Travel

If you feel you must travel during bad weather, Nancy put together a great list of iOS apps for surviving air travel. If you get stranded, check out Stuck at the Airport, which includes some of the unexpected services that can be found at airports worldwide.

Stay warm

While you’re out there in the weather, you might as well stay warm and connected. If you need pockets for your gear, check out the SCOTTEVEST hoodie that I’ve written about before. And use your touch-screen gear without freezing your fingers with conductive gloves from Agloves. The Agloves folks sent me some to try, and they’re surprisingly light and warm, and yes, I can use the touchscreen on my HTC Evo or iPod touch while wearing them.

Let’s hope that darn groundhog is right, and we’re in for an early spring. But in the meantime, I hope you can stay productive when “the weather outside is frightful.”

How do you keep working in extreme winter weather?

Image courtesy Rob Gutro, NASA’s Goddard Space Flight Center, Greenbelt, Md.

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Even with helpful software like Google’s new app, I don’t think BlackBerry will make a dent in the top-used devices as measured by some. Take, for instance, the most recent AdMob report that Om noted today at GigaOm. This quote is telling: “Between iPhone and the Android, I wonder if anyone else has a chance to even become a player on the mobile web.” A bold statement, yes, but it’s backed up by the data.

One BlackBerry device was in the top ten as measured by web requests on AdMob’s network of 15,000 mobile websites — the 8830 was barely above the Palm Pre. Granted, the data is based the AdMob platform, which likely has a far higher number of data points for iPhone and Android devices, and not BlackBerry units. But for all of the market share gains RIM has shown — see the trend in our recent infographic — the handsets don’t seem to be a driving force for the mobile web; Or at least not as much as other platforms are. Perhaps a new browser will change that?