Interesting article on the tools built/used by Google developers, and the woman who oversees the dev tool team. For the tool insights, jump to “For Google Eyes Only”.
“Google’s developer tools are, in some ways, a reflection of the egalitarian philosophy Meckfessel sees at play throughout the company. A single system, available from any company web browser, provides instant access to practically every piece of code that underpins practically every Google product and service. It even houses the code used to build, well, itself, in the kind of circular setup that’s so very common in the world of software.
The result is any Google engineer can tinker with code built by any other Google engineer. “The code is completely open — within the company,” Meckfessel says.
That doesn’t mean anyone can rewrite the code for, say, Gmail, compile it into executable software, and completely revamp the popular email service all on their own. But it does mean they can peruse and edit any of Gmail’s underlying code — and if they submit it to the right person for review and testing and compilation, they can indeed change the live service.”
“Everybody has this idea that Twitter is easy. With a little architectural hand waving we have a scalable Twitter, just that simple. Well, it’s not that simple as Raffi Krikorian, VP of Engineering at Twitter, describes in his superb and very detailed presentation on Timelines at Scale. If you want to know how Twitter works – then start here.”
“Welcome to my biography, 2013-style. It includes more data points than it possibly could have 20 years ago. And it’s part of a national obsession of a people who, literally, number our days. According to a recent nationwide survey for Pew Research Center Internet & American Life Project, 7 out of 10 people self-track regularly—using everything from human memory to a memory stick—some aspect of health for themselves or for someone else. Among the 3,000 adults questioned, the most popular things to monitor were weight and diet. A third of the people surveyed also track more esoteric elements of their health, from blood pressure to sleep to blood sugar. While many of them keep this information “in their heads,” a full 50 percent actually keep a written record of the data either using technology or on paper. According to the Consumer Electronics Association, in 2012 the U.S. sports and fitness category was a $70 billion business; and earlier this year, market firm ABI released a report that estimated that 485 million wearable computing devices—like smart watches and smart glasses—will be shipped annually by 2018.””
“The next change requires accepting messiness instead of insisting on clean, carefully curated data. “[In] an increasing number of situations, a bit of inaccuracy can be tolerated, because the benefits of using vastly more data of variable quality outweigh the costs of using smaller amounts of very exact data. . . When there was not that much data around, researchers had to make sure that the figures they bothered to collect were as exact as possible. Tapping vastly more data means that we can now allow some inaccuracies to slip in (provided the data set is not completely incorrect), in return for benefiting from the insights that a massive body of data provides.””
Research paper in Mary Ann Liebert, Big Data on Quantified Self. Big potential to aggregate individual data to make medical / biological discoveries and generate cures / remediations.
“A key contemporary trend emerging in big data science is the quantified self (QS)–individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual’s continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity—barriers to widespread adoption and a critique regarding scientific soundness—but these may be overcome.”
“Micro Service Architecture is an architectural concept that aims to decouple a solution by decomposing functionality into discrete services. Think of it as applying many of the principles of SOLID at an architectural level, instead of classes you’ve got services.
Conceptually speaking MSA is not particularly difficult to grasp but in practice it does raise many questions. How do these services communicate? What about latency between services? How do you test the services? How do you detect and respond to failure? How do you manage deployments when you have a bunch of interdependencies?”