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Does your application transmit customer information? Are there fields of sensitive customer data stored in your DB? Can your application be used on insecure networks? If so, you need a working knowledge of encryption and how to leverage Open Source APIs and libraries to make securing your data as easy as possible. Cryptography is quickly becoming a developer's new frontier of responsibility in many data-centric applications.

In today's data-sensitive and news-sensationalizing world, don't become the next headline by an inadvertent release of private customer or company data. Secure your persisted, transmitted and in-memory data and learn the terminology you'll need to navigate the ecosystem of symmetric and public/private key cryptography.

Now that you have the basics of encryption under your belt, we'll advance to talking about where it is sensible and performant to add this level of security to your application. Symmetric key and public key encryption have various levels of processing overhead, so you can't blindly just use the "best" encryption out there. What about password hashes? Did you know they are vulnerable with our "salt"?

We'll look at the performance metrics, security strength and weaknesses of various encryption algorithms. Given today's global economy, we'll also talk about what strength keys can and cannot be used across national borders. Lastly, we'll look at protocol-wrapping encryption techniques, such as VPNs, as a solution to abstracting away this difficult area of programming into a higher level service or device. We'll end with a brief peek at quantum and elliptic curve encryption.

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This session will survey a wide range of tools across the Java space. We'll look at utilities such as VisualVM, jstatd, jps, jhat, jmap, Eclipse Memory Analyzer, jtracert, btrace and more.

Open Source is not just a suite of libraries you consume within your application, but now reaches into the space of tools to help you troubleshoot and improve your applications. The price of these tools eliminates barriers to their use and their open source nature allows you to mix and match them into compositions that work well for your application's unique debugging needs.

These tools will help you peel away layers of your application to expose bugs and performance ceilings. We'll interactively analyze the heap and garbage collection cycles of both local and remote applications, take snapshots of heap, query the heap for heavy usage, leaks and augment running code without a reboot and without breaking a sweat. After attending, you'll never look at Java debugging the same way again.

Moore's law has finally hit the wall and CPU speeds have actually decreased in the last few years. The industry is reacting with hardware with an ever-growing number of cores and software that can leverage "grids" of distributed, often commodity, computing resources. But how is a traditional Java developer supposed to easily take advantage of this revolution? The answer is the Apache Hadoop family of projects. Hadoop is a suite of Open Source APIs at the forefront of this grid computing revolution and is considered the absolute gold standard for the divide-and-conquer model of distributed problem crunching. The well-travelled Apache Hadoop framework is curently being leveraged in production by prominent names such as Yahoo, IBM, Amazon, Adobe, AOL, Facebook and Hulu just to name a few.

In this session, you'll start by learning the vocabulary unique to the distributed computing space. Next, we'll discover how to shape a problem and processing to fit the Hadoop MapReduce framework. We'll then examine the incredible auto-replicating, redundant and self-healing HDFS filesystem. Finally, we'll fire up several Hadoop nodes and watch our calculation process get devoured live by our Hadoop cluster. At this talk's conclusion, you'll understand the suite of Hadoop tools and where each one fits in the aim of conquering large data sets.

With the basics of Hadoop under your belt, we'll dig into the depths of this amazing framework by writing our own reducer in Java and deploying it to the cluster. Next, we'll dig deeper into DSLs like Pig and its log-file processing cousin, Chukwa. Since grid topology is intentionally very opaque in Hadoop, we'll look at the benefits and how to achieve a properly tuned cluster with replication. Specific to HDFS, we'll tune the configurable parameters for storage redundancy and bucket sizes.

In the ultimate use case of using many Hadoop components in harmony, you'll find the need to have a centralized synchronization and coordination framework. Don't build these capabilities on your own though, as you might be tempted to do in a homegrown distributed system. Instead, leverage Hadoop ZooKeeper's ability to store small sub 1MB blocks of data that contain state, naming, and mutex information.

Explore what's new on the cutting edge release of Maven, version 3.0. We'll explore the performance improvements, features that make debugging Maven issues easier, and changes to POMs that may require modifications to your build, but will result in more determinate build outputs.

Maven 3.0 has undergone major refactorings, and correspondingly, a battery of backwards compatibility tests to ensure a smooth transition from Maven 2.0. These refactorings prepare Maven for the next several years of development, including the separation of the POM file language from from the POM in-memory processor, which is already leading to Groovy, Ruby and YAML based POM file parsers.