Our Philly Emerging Technologies for the Enterprise Conference (Philly ETE) returns for its 12th year after taking a year off. The conference itself started after the Chariot team had nine speakers at Java One, which at that time in 2003 many companies had cut back on travel due to the economic downturn. Technology changes very … Read More
Cloud computing has reduced engineering costs and improved delivery effectiveness drastically over the last decade, but new systems and features can still see lead times of weeks or months even for a prototype release, and operations costs are still often inefficiently managed. Modern approaches in cloud computing, including the new area of serverless architectures, tackle … Read More
When @WalmartLabs built Electrode, the React/Node.js application platform that powers the Walmart.com customer experience, performance was one of our primary concerns. React’s use of a virtual DOM and support for server-side rendering have earned it a reputation for speed, but in building our platform we encountered several unexpected performance bottlenecks. In this talk, we’ll discuss … Read More
Clojure programs are built from a small number of generic immutable data structures combined with a large number of pure functions that manipulate these generic data types. The Clojure development experience is characterized by a rapid, dynamic, and interactive REPL-driven experience. Clojure’s new spec library enhances Clojure by providing a system for stating concrete declarative … Read More
Apache Spark is one the most popular general purpose distributed systems in the past few years. Apache Spark has APIs in Scala, Java, Python and more recently a few different attempts to provide support for R, C#, and Julia. This talk looks at Apache Spark from a performance/scaling point of view and the work we … Read More
As core counts continue to increase, how we exploit hardware parallelism in practice shifts from concurrency — using more cores to handle more user requests — to parallelism — using more cores to solve data-intensive problems faster. This talk will explore the different goals, tools, and techniques involved between these various approaches, and how to analyze a computation for potential parallelism, with specific attention to the parallel stream library in Java 8.
This talk has two parts. First, I will present technical ideas from research, including my own, that help secure software by construction. Even though these are reasonable ideas, however, the gap between academia and industry often prevents these ideas from becoming realized in practice. Second, I will discuss what prevents longer-term security solutions from being commercialized, how we started the Cybersecurity Factory accelerator bridge the research/industry gap, and how we can work together to address the issues that remain.
Apps no longer just run on smartphones and tablets – they process verbal commands we speak to devices like Amazon Echo, run as bots in Slack channels, and are rapidly evolving customer experiences that span a range of IoT devices in homes, cars, offices, and industrial settings. Crucial to the success of all these ecosystems is one central idea: Code has to not just run in the cloud, it has to be easy to get it there and scale it there. Serverless computing – calling AWS Lambda functions instead of managing heavyweight applications on infrastructure – is changing how developers think about backends, event-driving processing, and application design. Infrastructure, deployment, and software platform setup that used to take days or weeks of time vanishes, replaced by microservices that do one thing well, require zero effort to deploy, and scale automatically and implicitly just by using them. At the same time, AWS Lambda and other serverless systems have redefined cloud economics by eliminating the possibility of cold servers, creating a radical new price point for applications running in the cloud and freeing developers and COO’s alike from worrying about paying for unused capacity. In this talk we’ll define Serverless computing, examine the key trends and innovative ideas behind the technology, and look in detail at design patterns for big data, event processing, mobile backends, and more using AWS Lambda.
In 2015, two years after its initial open source release, React took the position formerly held by Angular as the darling of the web. It’s used on some of the biggest sites in the world, such as Facebook, WhatsApp, Messenger, Instagram, Netflix, Airbnb, Uber, NFL, Dropbox, Asana, Atlassian, Khan Academy, Flipkart, Imgur, Reddit, Paypal, WalMart, WordPress, Wix, SquareSpace, etc.
Let’s be clear though: any UI you can build with React you can also build without React. React’s value proposition is that it simplifies your UI code, making it easier to build and maintain: it is declarative, component-based, uses one-way data flow, and has an API that most developers can become productive with in an afternoon. The people at Facebook have had so much success with it on the web that they thought – hey, wouldn’t it be great if we could write native mobile apps like this too? And so React Native was born, and the mobile development landscape will never look the same again.
When Apple open sourced Swift late last year, they invited the community into the discussion of where Swift should go and why. Instead of us having to imagine what the Swift language and library stewards and architects are thinking, we can read their words on the Swift evolution mailing list. In this talk we’ll look at what idiomatic Swift will look like soon when Swift 3 is soon released and talk about the reasoning behind some of the choices.
