The “Internet of Things” is the modern embodiment of an idea that has been around for a while — that not only can people be interconnected on the network, but that our appliances, belongings, buildings, and even clothing can be interconnected on the network. The IoT is attracting a great deal of attention, but it’s also pointing to issues with the current infrastructure of the Internet, and with applications that will need to be solved. In this talk, Colt McAnlis points to the problems with implementing the Internet of Things, and how we might need to think about and solve these problems in order to allow our refrigerator, bathrobe, and skateboard to speak with one another.
The book (and movie) Moneyball described how it’s possible to use metrics and statistics to squeeze the greatest possible results out of a low-budget operation. In this talk, Jeffrey Lembeck tells us that we can and should do the same thing for our Web applications. How does this work? What can (and should) we measure, and what should we do in response? If you want to do more with less (and all of us do), then you’ll want to watch this talk.
If you’re doing data science, then the odds are good that you’re using Python. And if you’re using Python to do data science, then you’re probably using matplotlib to create visualizations — charts, plots, and graphs that help us to make sense of the data you have collected and analyzed. In this talk, matplotlib lead developer Michael Droettboom introduces matplotilb, and shows how it can be used to create amazing charts, plots, and graphs in a variety of styles.
When we program on a Unix machine, we often use a terminal window. But what does a terminal program do? What is it trying to emulate, what features does it support, and how can you take advantage of it when you’re working? Why does it work the way it does, anyway? And using a bit of Python and understanding of what’s happening behind the scenes, what sorts of interesting things can we do? In this talk, Thomas Ballinger answers al of these questions, mixing his descriptions with extensive live-coding demos.
We keep hearing about big data and data science. But how can these disciplines be used with real-world cases? What sort of data is appropriate for data science? And how can we apply Python‘s many data-analysis libraries to these problems? In this talk, Robert Layton shows how he was able to predict sports winners using two of the most popular Python data-analysis libraries, Pandas and scikit-learn.
How do you store your secrets? This is, by definition, a sensitive issue; you want to make it easy for the right people to access secrets, but it should also be hard for others to get those secrets. And by “the right people,” we increasingly mean “the right programs,” in an age of devops and automated deployment. Plus, you need to worry about revoking privileges, or expiring access after a short time. One project that attempts to handle these problems is Vault, an open-source project developed by Hashicorp. In this talk, Seth Vargo describes Vault, and the problems that it attempts to solve. He also describes how Vault can be integrated into a data center, and then used by applications and deployment systems.
How does Git work? A lot of people who use Git work from a small set of commands, without actually knowing what is happening behind the scenes. This might not seem like a problem, but it is; knowing how Git works is essential to making it a seamless part of your development cycle. In this talk, Steve Smith describes how Git works, from its data structures to branches — and how we can get into (and out of) trouble when working with it. If you use Git, and you don’t quite understand how it works under the hood, then this talk will help you to make sense of things, and even improve your workflow.
It used to be so easy to set up an application: You set up a server, write some software, and then update the software over time. But as systems get bigger and more complex, it becomes much harder to write, test, and deploy large, monolithic systems. Over time, we have moved toward microservices, which have their own challenges. In this talk, James Lewis describes our migration toward microservices, and what that means for designing, testing, deploying, and just thinking about such applications. What has changed, what has become easier, and what has become harder?