What is DevOps (yet again)?

Empathy, communication, and collaboration across organizational boundaries.

Cropped image "Kilobot robot swarm" by asuscreative - Own work. Licensed under CC BY-SA 4.0 via Wikimedia Commons.http://commons.wikimedia.org/wiki/File:Kilobot_robot_swarm.JPG#mediaviewer/File:Kilobot_robot_swarm.JPG

I might try to define DevOps as the movement that doesn’t want to be defined. Or as the movement that wants to evade the inevitable cargo-culting that goes with most technical movements. Or the non-movement that’s resisting becoming a movement. I’ve written enough about “what is DevOps” that I should probably be given an honorary doctorate in DevOps Studies.

Baron Schwartz (among others) thinks it’s high time to have a definition, and that only a definition will save DevOps from an identity crisis. Without a definition, it’s subject to the whims of individual interest groups, and ultimately might become a movement that’s defined by nothing more than the desire to “not be like them.” Dave Zwieback (among others) says that the lack of a definition is more of a blessing than a curse, because it “continues to be an open conversation about making our organizations better.” Both have good points. Is it possible to frame DevOps in a way that preserves the openness of the conversation, while giving it some definition? I think so.

DevOps started as an attempt to think long and hard about the realities of running a modern web site, a problem that has only gotten more difficult over the years. How do we build and maintain critical sites that are increasingly complex, have stringent requirements for performance and uptime, and support thousands or millions of users? How do we avoid the “throw it over the wall” mentality, in which an operations team gets the fallout of the development teams’ bugs? How do we involve developers in maintenance without compromising their ability to release new software?

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Who should and should not be talking to your fridge?

A reflection on the social impacts of smarter hardware in the physical world.

Editor’s note: This is part of a series of posts exploring privacy and security issues in the Internet of Things. The series will culminate in a free webcast by the series author Dr. Gilad Rosner: Privacy and Security Issues in the Internet of Things will happen on February 11, 2015 — reserve your spot today.

My_Amazing_Fridge_Antonio_Roberts_FlickrHere’s the scenario today: I am out of milk, and my refrigerator sits there, mute and unsympathetic. Some time in the 90s, I was promised a fridge that would call the store when I was out of milk, and it would then be delivered while I, ignorant of my dearth of dairy, went about my business. Apparently such predictions were off. Someone forgot to tell my fridge manufacturer to put sensors, software, and networking gear into their products.

But there is hope. The dumb objects in the analog physical world are being slowly upgraded. From the very sexy telemetry systems in new BMWs to the very unsexy pallets of lettuce in a warehouse, Things That Heretofore Were Blind and Mute are getting eyes, ears, mouths, and in some cases, brains. This is evolution, not revolution, and while it is still slow-moving, it’s beneficial to reflect on some of the social impacts of smarter hardware in the physical world. Read more…

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A human-centered approach to data-driven design

The O'Reilly Radar Podcast: Arianna McClain on humanizing data-driven design, and Dirk Knemeyer on design in emerging tech.

This week on the O’Reilly Radar Podcast, O’Reilly’s Roger Magoulas talks with Arianna McClain, a senior hybrid design researcher at IDEO, about storytelling through data; the interdependent nature of qualitative and quantitative data; and the human-centered, data-driven design approach at IDEO.

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In their interview, Magoulas noted that in our research at O’Reilly, we’ve been talking a lot about the importance of the social science design element in getting the most out of data. McClain emphasized the importance of storytelling through data at IDEO and described IDEO’s human-centered approach to data-driven design:

“IDEO really believes in staying and remaining human-centered throughout the data journey. Starting off with, how might we measure something, how might we measure a behavior. We don’t sit in a room and come up with an algorithm or come up with a question. We start by talking to people. … We’re trying to build measures and survey questions to understand at scale how people make decisions. … IDEO remains data-driven to how we analyze and synthesize our findings. When we’re given a large data set, we don’t analyze it and write a report and give it to people and say, ‘This is the direction we think you should go.’

“Instead, we look at segmentations in the data, and stories in the data, and how the data clusters. Then we go back, and we try to find people who are representative of that cluster or that segmentation. The segmentations, again, are not based on demographic variables. They are based on needs and insights that we heard in our qualitative research. … What we’ve recognized is that something that seems so clear in the analysis is often very nuanced, and it can inform our design.”

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The evolution of GraphLab

The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.

Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.

I only really started playing around with GraphLab when the companion project GraphChi came onto the scene. By then I’d heard from many avid users and admired how their user conference instantly became a popular San Francisco Bay Area data science event. For this podcast episode, I sat down with Carlos Guestrin, co-founder/CEO of Dato, a start-up launched by the creators of GraphLab. We talked about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.

MATLAB for graphs

Guestrin remains a professor of computer science at the University of Washington, and GraphLab originated when he was still a faculty member at Carnegie Mellon. GraphLab was built by avid MATLAB users who needed to do large scale graphical computations to demonstrate their research results. Guestrin shared some of the backstory:

“I was a professor at Carnegie Mellon for about eight years before I moved to Seattle. A couple of my students, Joey Gonzales and Yucheng Low were working on large scale distributed machine learning algorithms specially with things called graphical models. We tried to implement them to show off the theorems that we had proven. We tried to run those things on top of Hadoop and it was really slow. We ended up writing those algorithms on top of MPI which is a high performance computing library and it was just a pain. It took a long time and it was hard to reproduce the results and the impact it had on us is that writing papers became a pain. We wanted a system for my lab that allowed us to write more papers more quickly. That was the goal. In other words so they could implement this machine learning algorithms more easily, more quickly specifically on graph data which is what we focused on.”

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It’s not just about Hadoop core anymore

For maximum business value, big data applications have to involve multiple Hadoop ecosystem components.

Data is deluging today’s enterprise organizations from ever-expanding sources and in ever-expanding formats. To gain insight from this valuable resource, organizations have been adopting Apache Hadoop with increasing momentum. Now, the most successful players in big data enterprise are no longer only utilizing Hadoop “core” (i.e., batch processing with MapReduce), but are moving toward analyzing and solving real-world problems using the broader set of tools in an enterprise data hub (often interactively) — including components such as Impala, Apache Spark, Apache Kafka, and Search. With this new focus on workload diversity comes an increased demand for developers who are well-versed in using a variety of components across the Hadoop ecosystem.

Due to the size and variety of the data we’re dealing with today, a single use case or tool — no matter how robust — can camouflage the full, game-changing potential of Hadoop in the enterprise. Rather, developing end-to-end applications that incorporate multiple tools from the Hadoop ecosystem, not just the Hadoop core, is the first step toward activating the disparate use cases and analytic capabilities of which an enterprise data hub is capable. Whereas MapReduce code primarily leverages Java skills, developers who want to work on full-scale big data engineering projects need to be able to work with multiple tools, often simultaneously. An authentic big data applications developer can ingest and transform data using Kite SDK, write SQL queries with Impala and Hive, and create an application GUI with Hue. Read more…

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Now available: Big Data Now, 2014 edition

Our wrap-up of important developments in the big data field.

In the four years we’ve been producing Big Data Now, our wrap-up of important developments in the big data field, we’ve seen tools and applications mature, multiply, and coalesce into new categories. This year’s free wrap-up of Radar coverage is organized around seven themes:

  • Cognitive augmentation: As data processing and data analytics become more accessible, jobs that can be automated will go away. But to be clear, there are still many tasks where the combination of humans and machines produce superior results.
  • Intelligence matters: Artificial intelligence is now playing a bigger and bigger role in everyone’s lives, from sorting our email to rerouting our morning commutes, from detecting fraud in financial markets to predicting dangerous chemical spills. The computing power and algorithmic building blocks to put AI to work have never been more accessible.
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