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Ratpack is a hyper-lightweight, Groovy-based web framework for developing and deploying simple apps in a hurry. Like its high-achieving cousin Gaelyk, it provides Groovy developers with a way to create web apps without days of iteration zero setup time.

In this talk, we'll look over Ratpack's very simple structure and live-code a small, practical example application. We'll look at how to evolve simple controller logic, how to manage templates, how to persist data, and how to deploy Ratpack applications to the web. The Java world needs ways to build small applications in a hurry, and Ratpack is the latest way to do it!

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Gradle. Another build tool? Come on! But before you say that, take a look at the one you are already using.

Whether your current tool is Make, Rake, Ant, or Maven, Gradle has a lot to offer. It leverages a strong object model like Maven, but a mutable, not predetermined one. Gradle relies on a directed acyclic graph (DAG) lifecycle like Maven, but one that can be customized. Gradle offers imperative build scripting when you need it (like Ant), but declarative build approaches by default (like Maven). In short, Gradle believes that conventions are great -- as long as they are headed in the same direction you need to go. When you need to customize something in your build, your build tool should facilitate that with a smile, not a slap in the face. And customizations should be in a low-ceremony language like Groovy. Is all this too much to ask?

Gradle has received the attention of major open source efforts and has chalked up significant conversions by the Spring Integration, Hibernate, and Grails projects. What do these technology leaders see in this bold new build tool? They see not only a better way to build Java applications, but an extensive ecosystem of connecting to existing Ant and Maven build files while expanding the horizon of test, CI, and deployment automation in an easy manner. Join us for 90 minutes and let us take you on this same walk of discovery of the most innovative build tool you've ever seen'.

Winston Churchill famously said, "First we shape our buildings, and afterwards, our buildings shape us." He was talking about the reconstruction of the House of Parliament, which was damaged in a bombing raid in World War II. There was a debate about how to shape the chamber to best accommodate the deliberative activity of the body that met in it. Churchill was talking about buildings, but it turns out his insight is a very general one indeed.

Developers are constantly debating their choices of language, platform, editor, methodology, and even where to put the curly braces. The robust internal dialog in community is a healthy thing, but our debates are often focused on the wrong topics. Have you ever compared languages by performance benchmarks? Platforms by alleged claims of developer productivity? Methodologies by feature velocity? There is a very good chance you're doing it wrong.

Rather than focus on the material content of our debates—language performance, editor productivity, methodological velocity—we should take Churchill's advice and think about the form of our choices. How will our choice of language influence the way we solve future problems? What assumptions does our methodology make about the nature of work? How will our choice of database affect the kinds of problems we think of as solvable?

Drawing on lessons from building architecture, literature, music, the visual arts, and even software itself, we'll learn the priority of interpreting the form of things before attempting to understand their content. You may never look at software architecture the same way again.

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Alternative databases continue to establish their role in the technology stack of the future—and for many, the technology stack of the present. Making mature engineering decisions about when to adopt new products is not easy, and requires that we learn about them both from an abstract perspective and from a very concrete one as well. If you are going to recommend a NoSQL database for a new project, you're going to have to look at code.

In this talk, we'll examine three important contenders in the NoSQL space: Cassandra, MongoDB, and Neo4J. We'll review their data models, scaling paradigms, and query idioms. Most importantly, we'll work through the exercise of modeling a real-world problem with each database, and look at the code and queries we'd use to implement real product features. Come to this session for a thorough and thoroughly practical smackdown between three important NoSQL products.

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Want to go deep on a popular NoSQL database? Cassandra is a scalable, highly available, column-oriented data store in use at Netflix, Twitter, Reddit, Rackspace, and other web-scale operations. It offers a compelling combination of a rich data model, a robust deployment track record, and a sound architecture, making it a good choice of NoSQL databases to study first.

In this session, we'll talk about Cassandra's data model, look at its query idioms, talk about how to deploy it, and look at use cases in which it is an appropriate data storage solution. We'll study its origins in the Amazon Dynamo project and Google's BigTable, and learn how its architecture helps us achieve the gold standard of scalability: horizontal scalability on commodity hardware. You'll leave prepared to begin experimenting with Cassandra immediately and planning its adoption in your next project.

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Alistair Cockburn has described software development as a game in which we choose among three moves: invent, decide, and communicate. Most of our time at No Fluff is spent learning how to be better at inventing. Beyond that, we understand the importance of good communication, and take steps to improve in that capacity. Rarely, however, do we acknowledge the role of decision making in the life of software teams, what can cause it to go wrong, and how to improve it.

In this talk, we will explore decision making pathologies and their remedies in individual, team, and organizational dimensions. We'll consider how our own cognitive limitations can lead us to to make bad decisions as individuals, and what we might do to compensate for those personal weaknesses. We'll learn how a team can fall into decision-making dysfunction, and what techniques a leader might employ to healthy functioning to an afflicted group. We'll also look at how organizational structure and culture can discourage quality decision making, and what leaders to swim against the tide.

Software teams spend a great deal of time making decisions that place enormous amounts of capital on the line. Team members and leaders owe it to themselves to learn how to make them well.