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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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Neo4j is an open-source, enterprise-class database with a conventional feature set and a very unconventional data model. Like the databases we're already used to, it offers support for Java, ACID transactions, and a feature-rich query language. But before you get too comfortable, you have to wrap your mind around its most important feature: Neo4j is a graph database, built precisely to store graphs efficiently and traverse them more performantly than relational, document, or key/value databases ever could.

Neo4j is an obvious fit to anyone who thinks they have a graph problem to solve, but this is not many people. It turns out that the most interesting property of Neo4j is its architectural agenda. It wants you to think of the entire world as a graph—as a set of connected information resources. Steeped in the thinking of resource oriented architecture, this NoSQL database wants to change the way you look at your world, and unlock new value in your data as a result.

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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.

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Git is a version control system you may have been hearing a bit about lately. But simply hearing more about it may not be enough to convince you of its value. Getting hands on experience is what really counts. In this workshop, you'll bring your Windows, Mac or Linux laptop and walk through downloading, installing, and using Git in a collaborative fashion.

The workshop style of this class will allow you to observe and discover the value of this new version control tool first hand. You'll be cloning, creating, commiting, and pushing repositories by the conclusion of this session.

PreReq: Basic knowledge of a version control system. Subversion knowledge is a plus, but not imperative.

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When you want to measure fractions of a millimeter, you get a micrometer. When you want to measure centimeters, you get a ruler. When you want to measure kilometers, you might use a laser beam. The abstract task is the same in all cases, but the tools differ significantly based on the size of the measurement.

Likewise, there are some computations that can be done quickly on data structures that fit into memory. Some can't fit into memory, but will fit on the direct-attached disk of a single computer. But when you've got many terabytes or even petabytes of data, you need tooling adapted to the scale of the task. Enter Hadoop.

Hadoop is a widely-used open source framework for storing massive data sets in distributed clusters of computers and efficiently distributing computational tasks around the cluster. Come learn about the Hadoop File System (HDFS), the MapReduce pattern and its implementation, and the broad ecosystem of tools, products, and companies that have grown up around this ground-breaking project.

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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!