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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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Take one ugly legacy schema, a toolbox full of simple database refactorings, and a world-class schema refactoring tool, and you've got 90 minutes of workshop that will equip you to bring a culture of database responsibility to your team.

In this workshop, we'll start with a live schema in need of some help, and slowly improve it in a controlled fashion using Liquibase. We'll see how to create and alter tables, add constraints, drop columns, control changes in stored procedures, and more. You should come away with a solid understanding of how to use the tool and how to integrate it into your team's development, build, and deployment processes.

Attendees should already have a conversational understanding of Liquibase, or have attended Scripting the Schema with Liquibase session. Please bring a laptop or be prepared to pair with a friend.

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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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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.