"Big Data" entries

Four short links: 10 April 2014

Four short links: 10 April 2014

Rise of the Patent Troll, Farm Data, The Block Chain, and Better Writing

  1. Rise of the Patent Troll: Everything is a Remix (YouTube) — primer on patent trolls, in language anyone can follow. Part of the fixpatents.org campaign. (via BoingBoing)
  2. Petabytes of Field Data (GigaOm) — Farm Intelligence using sensors and computer vision to generate data for better farm decision making.
  3. Bullish on Blockchain (Fred Wilson) — our 2014 fund will be built during the blockchain cycle. “The blockchain” is bitcoin’s distributed consensus system, interesting because it’s the return of p2p from the Chasm of Ridicule or whatever the Gartner Trite Cycle calls the time between first investment bubble and second investment bubble under another name.
  4. Hemingway — online writing tool to help you make your writing clear and direct. (via Nina Simon)
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The backlash against big data, continued

Ignore the hype. Learn to be a data skeptic.

Yawn. Yet another article trashing “big data,” this time an op-ed in the Times. This one is better than most, and ends with the truism that data isn’t a silver bullet. It certainly isn’t.

I’ll spare you all the links (most of which are much less insightful than the Times piece), but the backlash against “big data” is clearly in full swing. I wrote about this more than a year ago, in my piece on data skepticism: data is heading into the trough of a hype curve, driven by overly aggressive marketing, promises that can’t be kept, and spurious claims that, if you have enough data, correlation is as good as causation. It isn’t; it never was; it never will be. The paradox of data is that the more data you have, the more spurious correlations will show up. Good data scientists understand that. Poor ones don’t.

It’s very easy to say that “big data is dead” while you’re using Google Maps to navigate downtown Boston. It’s easy to say that “big data is dead” while Google Now or Siri is telling you that you need to leave 20 minutes early for an appointment because of traffic. And it’s easy to say that “big data is dead” while you’re using Google, or Bing, or DuckDuckGo to find material to help you write an article claiming that big data is dead. Read more…

Comments: 6

5 Fun Facts about HBase that you didn’t know

HBase has made inroads in companies across many industries and countries

With HBaseCon right around the corner, I wanted to take stock of one of the more popular1 components in the Hadoop ecosystem. Over the last few years, many more companies have come to rely on HBase to run key products and services. The conference will showcase a wide variety of such examples, and highlight some of the new features that HBase developers have added over the past year. In the meantime here are some things2 you may not have known about HBase:

Many companies have had HBase in production for 3+ years: Large technology companies including Trend Micro, EBay, Yahoo! and Facebook, and analytics companies RocketFuel and Flurry depend on HBase for many mission-critical services.

There are many use cases beyond advertising: Examples include communications (Facebook messages, Xiaomi), security (Trend Micro), measurement (Nielsen), enterprise collaboration (Jive Software), digital media (OCLC), DNA matching (Ancestry.com), and machine data analysis (Box.com). In particular Nielsen uses HBase to track media consumption patterns and trends, mobile handset company Xiaomi uses Hbase for messaging and other consumer mobile services, and OCLC runs the world’s largest online database of library resources on HBase.

Flurry has the largest contiguous HBase cluster: Mobile analytics company Flurry has an HBase cluster with 1,200 nodes (replicating into another 1,200 node cluster). Flurry is planning to significantly expand their large HBase cluster in the near future.

Read more…

Comment: 1
Four short links: 2 April 2014

Four short links: 2 April 2014

Fault-Tolerant Resilient Yadda Yadda, Tour Tips, Punch Cards, and Public Credit

  1. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (PDF) — Berkeley research paper behind Apache Spark. (via Nelson Minar)
  2. Angular Tour — trivially add tour tips (“This is the widget basket, drag and drop for widget goodness!” type of thing) to your Angular app.
  3. Punchcard — generate Github-style punch card charts “with ease”.
  4. Where Credit Belongs for Hack (Bryan O’Sullivan) — public credit for individual contributors in a piece of corporate open source is a sign of confidence in your team, that building their public reputation isn’t going to result in them leaving for one of the many job offers they’ll receive. And, of course, of caring for your individual contributors. Kudos Facebook.
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Wearable intelligence

Establishing protocols to socialize wearable devices.

The age of ubiquitous computing is accelerating, and it’s creating some interesting social turbulence, particularly where wearable hardware is concerned. Intelligent devices other than phones and screens — smart headsets, glasses, watches, bracelets — are insinuating themselves into our daily lives. The technology for even less intrusive mechanisms, such as jewelry, buttons, and implants, exists and will ultimately find commercial applications.

And as sensor-and-software-augmented devices and wireless connections proliferate through the environment, it will be increasingly difficult to determine who is connected — and how deeply — and how the data each of us generates is disseminated, captured and employed. We’re already seeing some early signs of wearable angst: recent confrontations in bars and restaurants between those wearing Google Glass and others worried they were being recorded.

This is nothing new, of course. Many major technological developments experienced their share of turbulent transitions. Ultimately, though, the benefits of wearable computers and a connected environment are likely to prove too seductive to resist. People will participate and tolerate because the upside outweighs the downside. Read more…

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Four short links: 31 March 2014

Four short links: 31 March 2014

Game Patterns, What Next, GPU vs CPU, and Privacy with Sensors

  1. Game Programming Patterns — a book in progress.
  2. Search for the Next Platform (Fred Wilson) — Mobile is now the last thing. And all of these big tech companies are looking for the next thing to make sure they don’t miss it.. And they will pay real money (to you and me) for a call option on the next thing.
  3. Debunking the 100X GPU vs. CPU Myth — in Pete Warden’s words, “in a lot of real applications any speed gains on the computation side are swamped by the time it takes to transfer data to and from the graphics card.”
  4. Privacy in Sensor-Driven Human Data Collection (PDF) — see especially the section “Attacks Against Privacy”. More generally, it is often the case the data released by researches is not the source of privacy issues, but the unexpected inferences that can be drawn from it. (via Pete Warden)
Comments: 2
Four short links: 28 March 2014

Four short links: 28 March 2014

Javascript on Glass, Smart Lights, Hardware Startups, MySQL at Scale

  1. WearScript — open source project putting Javascript on Glass. See story on it. (via Slashdot)
  2. Mining the World’s Data by Selling Street Lights and Farm Drones (Quartz) — Depending on what kinds of sensors the light’s owners choose to install, Sensity’s fixtures can track everything from how much power the lights themselves are consuming to movement under the post, ambient light, and temperature. More sophisticated sensors can measure pollution levels, radiation, and particulate matter (for air quality levels). The fixtures can also support sound or video recording. Bring these lights onto city streets and you could isolate the precise location of a gunshot within seconds.
  3. An Investor’s Guide to Hardware Startups — good to know if you’re thinking of joining one, too.
  4. WebScaleSQL — a MySQL downstream patchset built for “large scale” (aka Google, Facebook type loads).
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Four short links: 24 March 2014

Four short links: 24 March 2014

Google Flu, Embeddable JS, Data Analysis, and Belief in the Browser

  1. The Parable of Google Flu (PDF) — We explore two
    issues that contributed to [Google Flu Trends]’s mistakes—big data hubris and algorithm dynamics—and offer lessons for moving forward in the big data age.
    Overtrained and underfed?
  2. Duktape — a lightweight embeddable Javascript engine. Because an app without an API is like a lightbulb without an IP address: retro but not cool.
  3. Principles of Good Data Analysis (Greg Reda) — Once you’ve settled on your approach and data sources, you need to make sure you understand how the data was generated or captured, especially if you are using your own company’s data. Treble so if you are using data you snaffled off the net, riddled with collection bias and untold omissions. (via Stijn Debrouwere)
  4. Deep Belief Networks in Javascript — just object recognition in the browser. The code relies on GPU shaders to perform calculations on over 60 million neural connections in real time. From the ever-more-awesome Pete Warden.
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Podcast: thinking with data

Data tools are less important than the way you frame your questions.

Max Shron and Jake Porway spoke with me at Strata a few weeks ago about frameworks for making reasoned arguments with data. Max’s recent O’Reilly book, Thinking with Data, outlines the crucial process of developing good questions and creating a plan to answer them. Jake’s nonprofit, DataKind, connects data scientists with worthy causes where they can apply their skills.

A few of the things we talked about:

  • The importance of publishing negative scientific results
  • Give Directly, an organization that facilitates donations directly to households in Kenya and Uganda. Give Directly was able to model income using satellite data to distinguish thatched roofs from metal roofs.
  • Moritz Stefaner calling for a “macroscope”
  • Project Cybersyn, Salvador Allende’s plan for encompassing the entire Chilean economy in a single real-time computer system
  • Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott

After we recorded this podcast episode at Strata Santa Clara, Max presided over a webcast on his book that’s archived here.

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Four short links: 18 March 2014

Four short links: 18 March 2014

On Managers, Human Data, Driverless Cars, and Bad Business

  1. On Managers (Mike Migurski) — Managers might be difficult, hostile, or useless, but because they are parts of an explicit power structure they can be evaluated explicitly.
  2. Big Data: Humans Required (Sherri Hammons) — the heart of the problem with data: interpretation. Data by itself is of little value. It is only when it is interpreted and understood that it begins to become information. GovTech recently wrote an article outlining why search engines will not likely replace actual people in the near future. If it were merely a question of pointing technology at the problem, we could all go home and wait for the Answer to Everything. But, data doesn’t happen that way. Data is very much like a computer: it will do just as it’s told. No more, no less. A human is required to really understand what data makes sense and what doesn’t. (via Anne Zelenka)
  3. Morgan Stanley on the Economic Benefits of Driverless CarsThe total savings of over $5.6 trillion annually are not envisioned until a couple of decades as Morgan Stanley see four phases of adoption of self-driving vehicles. Phase 1 is already underway, Phase 2 will be semi-autonomous, Phase 3 will be within 5 to 10 years, by which time we will see fully self-driving vehicles on the roads – but not widespread usage. The authors say Phase 4, which will have the biggest impact, is when 100% of all vehicles on the roads will be fully autonomous, they say this may take a couple of decades.
  4. Worse (Marco Arment) — I’ve been sitting on this but can’t fault it. In the last few years, Google, Apple, Amazon, Facebook, and Twitter have all made huge attempts to move into major parts of each others’ businesses, usually at the detriment of their customers or users.
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