Dave Zwieback on learning reviews and humans keeping pace with complex systems

O'Reilly Radar Podcast: Learning from both failure and success to make our systems more resilient.

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In this week’s Radar Podcast episode, I chat with Dave Zwieback, head of engineering at Next Big Sound and CTO of Lotus Outreach. Zwieback is the author of a new book, Beyond Blame: Learning from Failure and Success, that outlines an approach to make postmortems not only blameless, but to turn them into a productive learning process. We talk about his book, the framework for conducting a “learning review,” and how humans can keep pace with the growing complexity of the systems we’re building.

When you add scale to anything, it becomes sort of its own problem. Meaning, let’s say you have a single computer, right? The mean time to failure of the hard drive or the computer is actually fairly lengthy. When you have 10,000 of them or 10 million of them, you’re having tens if not hundreds of failures every single day. That certainly changes how you go about designing systems. Again, whenever I say systems, I also mean organizations. To me, they’re not really separate.

I spent a bunch of my time in fairly large-scale organizations, and I’ve witnessed and been part of a significant number of outages or issues. I’ve seen how dysfunctional organizations dealing with failure can be. By the way, when we mention failure, it’s important for us not to forget about success. All the things that we find in the default ways that people and organizations deal with failure, we find in the default ways that they deal with success. It’s just a mirror image of each other.

We can learn from both failures and success. If we’re only learning from failures, which is what the current practice of postmortem is focused on, then we’re missing … the other 99% of the time when they’re not failing. The practice of learning reviews allows for learning from both failures and successes.

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Bob Baxley on Apple and Pinterest, company cultures, and the designer shortage

The O’Reilly Design Podcast: Culture, competition, and design staffing.

Subscribe to the O’Reilly Design Podcast, our podcast exploring how experience design—and experience designers—are shaping business, the Internet of Things, and other domains.

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In this week’s Design Podcast episode, I sit down with Bob Baxley, who is keynoting at OReilly’s inaugural Design Conference. He compares cultures at Apple and Pinterest, talks about competition in the design playing field, and addresses the designer shortage.

Here are a few highlights from our conversation:

My observation is that although Apple really dominates the product culture of technology, certainly in Silicon Valley potentially globally, it’s really the Google culture that dominates how companies work.

By that I mean, a culture of engineering-centric, ship fast, let’s fix stuff, intense incrementalism based on metrics and experimentation, which is very different from how Apple works, at least in the time I was there, where it was much more deterministic. I think the difference maybe has to do with the business models, where Apple is creating a product that they’re going to sell and somebody has to pay money for.

Pinterest, I think, is trying to sit in between those two right now. The foundational DNA of Pinterest is definitely Google, where Ben Silverman, the founder and CEO, was before he started Pinterest, and then Evan Sharp, who’s the creative co-founder. Evan was at Facebook, and my experience of Pinterest is that it’s really in between a Google and Facebook culture—a lot of emphasis on engineering, but still a lot of input from product management and obviously design; having a design co-founder influences the company a lot.

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Robert Brunner on designing and building great products

The O’Reilly Hardware Podcast: The critical role of design in creating iconic products and brands.

Subscribe to the O’Reilly Hardware Podcast for insight and analysis about the Internet of Things and the worlds of hardware, software, and manufacturing.

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Our expectations for industrial design have risen immeasurably in the last decade. Think of any piece of consumer electronics from 2005—a BlackBerry, for instance—and you’ll think of something that was encased in plastic painted silver to imitate metal, with a too-light heft and a rattle when shaken.

Now, nearly every successful piece of consumer hardware is the result of careful design and exquisite manufacturing. Apple deserves a great deal of credit for that shift by resetting the baseline with the iPhone in 2007, but new tools and processes have played an important role as well. Digital design has become easy and sophisticated, and contract manufacturers can do spectacular things with glass, aluminum, and semiconductors that were nearly impossible just a few years ago.

Our guest on this week’s episode of the O’Reilly Hardware Podcast is Robert Brunner, a founder of this new era of design. Brunner was director of industrial design at Apple from 1989 to 1996, overseeing the design of the PowerBook. He was the chief designer of Beats by Dr. Dre, the design-driven line of headphones that Apple acquired for $3 billion last year. And he’s the founder of Ammunition, which has worked with startups and large companies on a wide range of innovative consumer products.

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Jeff Jonas on context computing, irresistible surveillance, and hunting astroids with Space Time Boxes

The O'Reilly Radar Podcast: Context-aware computing, privacy by design, and predicting astroid collisions.

Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

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In this week’s Radar Podcast episode, I sit down with Jeff Jonas, an IBM fellow and chief scientist of context computing, Ironman triathlete, and contributing author to the new book, Privacy in the Modern Age: The Search for Solutions. Jonas talks about applications of context-aware computing, his new G2 software, and astroid hunting with astronomers at the University of Honolulu.

Here are a few highlights from our conversation:

The definition I’m using of context is this: to better understand something by taking into account the things around it. Context computing is taking a new piece of data that arrived in the enterprise as a puzzle piece and finding other pieces of data that had been previously seen and see how it fits. Instead of using algorithms staring at puzzle pieces, you end up with whole chunks of the puzzle and it’s much easier to make a high-quality prediction.

The purpose of G2 is to be able to take structured and unstructured data from batch or streaming sources. Think of it as new observations across a virtually unlimited number of data points. You could think of this as internet of things feeding it or transactional systems or social data or mobile data. It’s about weaving all those puzzle pieces together and then using the puzzle pieces as they land to figure out what’s important or not and use these system to help focus people’s attention.

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Ari Gesher and Kipp Bradford on security and the Internet of Things

The O’Reilly Hardware Podcast: Evolving expectations for privacy.

Subscribe to the O’Reilly Hardware Podcast for insight and analysis about the Internet of Things and the worlds of hardware, software, and manufacturing.

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In this episode of our newly renamed Hardware Podcast, I talk with Ari Gesher, engineering ambassador at Palantir Technologies, and Kipp Bradford, research scientist at the MIT Media Lab.

Gesher is the co-author of The Architecture of Privacy: On Engineering Technologies that Can Deliver Trustworthy Safeguards. Bradford is co-author of Distributed Network Data: From Hardware to Data to Visualization, and he’s spoken twice at Solid.

Discussion points:

  • The difference between security and privacy
  • Ari’s notion of what it means to be “polite” in a world where everything is recorded
  • The need and rationale for standards and protocols for IoT devices

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Graph databases are powering mission-critical applications

The O’Reilly Data Show Podcast: Emil Eifrem on popular applications of graph technologies, cloud computing, and company culture.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

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While most people associate graphs with social media analysis, there are a wide range of applications — including recommendations, fraud detection, I.T. operations, and security — that are routinely framed using graphs. This wide variety of use cases has led to rise to many interesting tools for storing, managing, visualizing, and analyzing massive graphs. The important thing to note is that graph databases are not limited to reporting and analytics, but are also being used to power mission critical applications.

In this episode of the O’Reilly Data Show, I sat down with Emil Eifrem, CEO and co-founder of Neo Technology. We talked about the early days of NoSQL, applications of graph databases, cloud computing, and company culture in the U.S. and Sweden.

Graph and NoSQL databases

The relational database had been an accelerator, and here it’s really slowing us down. What we ended up concluding was that the problem was this mismatch between the shape of the data and the abstractions that were exposed by our infrastructure. At that point, we said, okay, what if we had a database that just exposed these amazing network-oriented data structures or graph-oriented data structures, but other than that, had all the properties of a relational database. Wouldn’t that be great? …  Ultimately, we said the famous last words: ‘Hey, let’s just build it ourselves. How hard can it be?’ It turns out it’s 15 years later!

2007 is when both the Dynamo paper had been published and the BigTable paper had been published out of Amazon and Google, respectively. That’s when, in early adopter circuits, the discourse started to change … maybe the era of the one-size-fits-all database is over. Maybe our job isn’t to take all of our data and shove it through a relational database. Maybe there are some other tools and technologies and abstractions out there that make better sense for some data. That was in ’07.  I really think it was as if lightning struck in the community. … . [Dynamo and BigTable were announced] and the next day, 12 open source projects, implementing it, and then the next day, 24 new ones. It was just crazy back then.

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