With the new release cadence, programmers can enjoy new features at a much faster rate. Java has been evolving rapidly, but in a responsible manner. While some of the new recent features in Java have been around in other languages for a while there are some coolness for them being introduced in Java. First, programmers who are maintaining large legacy systems can make use of these exciting features, right there in Java as they move forward to adopting newer versions. Second, Java brings its own twist and innovation to how these features are used in the language. Learning these is not only an exciting journey but can also make us more productive when writing Java code.

In this presentation we will take a live coding, example driven approach to look at a few features that have been added to Java in the most recent releases of the language.

Multithreading has been in Java from day one. The multithreading API has gone through significant changes over the years. And yet, we have something major that's threading again. What's the reason for yet another implementation, yet another change? How is that different from what we already have. When will we use the new model and when will we stick to the existing APIs.

Too many questions but we will not take them all in parallel. Instead we will give the questions serious thoughts and get a deeper understanding of the purpose of Project Loom, what problems it solves, and how and when we can benefit from it.

I like to call Reactive Programming as Functional Programming++. There is so much similarity between FP and Rx and yet there are some significant differences. In this hands on workshop we will dive into what Reactive Programming is, learn about the APIs and capabilities, using RxJava as a vehicle.

While the examples will use RxJava, the concepts are general enough that you can readily benefit from the content, like backpressure and error handling, even if you are using other reactive libraries.

I like to call Reactive Programming as Functional Programming++. There is so much similarity between FP and Rx and yet there are some significant differences. In this hands on workshop we will dive into what Reactive Programming is, learn about the APIs and capabilities, using RxJava as a vehicle.

While the examples will use RxJava, the concepts are general enough that you can readily benefit from the content, like backpressure and error handling, even if you are using other reactive libraries.

You have some modular code with a REST API. You are on your way to Microservices. Next, you package it in a container image that others can run. Simple. Now what? Your service needs to log information, needs to scale and load balance between its clones. Your service needs environment and metadata way outside its context. What about where the service will run? Who starts it? What monitors its health? What about antifragility? Updates? Networking? Oh my.

Don't get flustered. We will explore how Kubernetes simplifies the complexity of distributed computing.

This session will help you understand the terms, architecture and the mechanics of the Kubernetes tools. You will understand how to target your applications to a seemingly complex distributed compute platform.

Prerequisite: If you are unfamiliar with Kubernetes be sure to attend: Kubernetes Koncepts (1 of 2)

Aha moments with apps in containers can be quite liberating. The mobile space is saturated with “there's an app for that”. For us, we now expect “there's a container for that”. “Write once, run anywhere” (WORA) has changed to “Package once, run anywhere” (PORA). The growing community of containers is riding up the hype curve. We will look at many ways to assemble pods using architecture patterns you already know.

Your software package delivery and installation is no longer an rpm, deb, dmg, jar, war, native executable or a run script, it is simply an image that has a common run container command.

During the presentation, we will explore some examples on Katacoda.

A Docker image is the artifact of the container world. Leaner images allow easier for quicker build times, less resource management (disk pressure and network usage), fewer attack vectors, and better performance when pulling or pushing images for storage or upon deployment. Lean images also produce smaller containers, which in turn require fewer resources at runtime, allowing for higher server density. Multistage Dockerfiles can help reduce the complexity of CI/CD pipelines by reducing the number of moving parts in building, testing, and producing a production-grade image. The key to building leaner (smaller) images, with little build-time overhead is to understand how Docker uses the Union File System (UFS), how Docker builds (and when it busts) the cache, and how to use the Dockerfile specification to it's fullest potential.

In this exercises driven, hands-on workshop, we will dive deep, peeking under the hood to get a glimpse of the Union File System, and then proceed to look at the effects of many of the important Dockerfile instructions. We will see how best to use them, and highlight any caveats that we should be aware of.

By the end of this class you will have gained a keen understanding of how best to write your Dockerfiles, and effectively build and design lean images, and containers.

A Docker image is the artifact of the container world. Leaner images allow easier for quicker build times, less resource management (disk pressure and network usage), fewer attack vectors, and better performance when pulling or pushing images for storage or upon deployment. Lean images also produce smaller containers, which in turn require fewer resources at runtime, allowing for higher server density. Multistage Dockerfiles can help reduce the complexity of CI/CD pipelines by reducing the number of moving parts in building, testing, and producing a production-grade image. The key to building leaner (smaller) images, with little build-time overhead is to understand how Docker uses the Union File System (UFS), how Docker builds (and when it busts) the cache, and how to use the Dockerfile specification to it's fullest potential.

In this exercises driven, hands-on workshop, we will dive deep, peeking under the hood to get a glimpse of the Union File System, and then proceed to look at the effects of many of the important Dockerfile instructions. We will see how best to use them, and highlight any caveats that we should be aware of.

By the end of this class you will have gained a keen understanding of how best to write your Dockerfiles, and effectively build and design lean images, and containers.

You have been using Git for a while. You know how to stage and commit your work, create and delete branches and collaborate with your team members using remotes. But Git often leaves your confused — ever committed to your work to the wrong branch? Even worse, ever accidentally delete a branch that you needed to keep around? And what is God's good name is “Detached HEAD state”? Why tag commits, when we have branches? Is there a better work-flow than just using merges? What's the difference between a merge and a rebase?

The answer to all of these questions, and more, lies in the constitution of a commit, and the directed acyclic graph (DAG) that Git uses to manage your history. This, right here, is the key to understanding everything in Git.

In this hands-on workshop, we will level up your Git skills. We will foray into the underbelly of Git, and reveal the mystery behind the arcane interface that is the Git CLI.

By the end of this workshop, you will have a keen understanding on how best to use Git, as well as know how to dig yourself any prickly situation you might find yourself in. You will become your team's hero(ine). Most importantly, you will walk away with a keen appreciation of how beautiful and elegant Git really is.

You have been using Git for a while. You know how to stage and commit your work, create and delete branches and collaborate with your team members using remotes. But Git often leaves your confused — ever committed to your work to the wrong branch? Even worse, ever accidentally delete a branch that you needed to keep around? And what is God's good name is “Detached HEAD state”? Why tag commits, when we have branches? Is there a better work-flow than just using merges? What's the difference between a merge and a rebase?

The answer to all of these questions, and more, lies in the constitution of a commit, and the directed acyclic graph (DAG) that Git uses to manage your history. This, right here, is the key to understanding everything in Git.

In this hands-on workshop, we will level up your Git skills. We will foray into the underbelly of Git, and reveal the mystery behind the arcane interface that is the Git CLI.

By the end of this workshop, you will have a keen understanding on how best to use Git, as well as know how to dig yourself any prickly situation you might find yourself in. You will become your team's hero(ine). Most importantly, you will walk away with a keen appreciation of how beautiful and elegant Git really is.

The Spring Framework transformed enterprise Java development nearly two decades ago, making it easier to achieve common things such as transactions, security, loose-coupling, and much more Over the years, Spring has continued to rise to every challenge Java developers face, most recently addressing subjects such as reactive programming, cloud computing, and container deployment in Kubernetes. Meanwhile, Spring Boot makes easy work of Spring by employing (among other things) auto-configuration, runtime insight and management, and a practical convention for specifying application properties.

The releases of Spring Framework 6 and Spring Boot 3 bring exciting and useful new capabilities. With features like native compilation, improved observability and tracing, support for HTTP problem details, and declarative HTTP clients, as well as baselining on Java 17 and Jakarta EE 9, Spring is ready for a new generation of application development.

In this workshop, you'll start with a very simple Spring Boot application and learn to grow it into a fully functional application including a web front-end and data persistence. And you'll get hands-on experience with some of the most exciting new features in Spring 6 and Spring Boot 3.

Workshop Requirements

You'll need…

• Java (17 or higher)
• An IDE of your choosing (I'll be using either Spring Tools or IntelliJ, but VSCode or even NetBeans will be fine if you prefer)
• A web browser (who doesn't have one of those?)
• Docker Desktop (https://www.docker.com/products/docker-desktop/)

The Spring Framework transformed enterprise Java development nearly two decades ago, making it easier to achieve common things such as transactions, security, loose-coupling, and much more Over the years, Spring has continued to rise to every challenge Java developers face, most recently addressing subjects such as reactive programming, cloud computing, and container deployment in Kubernetes. Meanwhile, Spring Boot makes easy work of Spring by employing (among other things) auto-configuration, runtime insight and management, and a practical convention for specifying application properties.

The releases of Spring Framework 6 and Spring Boot 3 bring exciting and useful new capabilities. With features like native compilation, improved observability and tracing, support for HTTP problem details, and declarative HTTP clients, as well as baselining on Java 17 and Jakarta EE 9, Spring is ready for a new generation of application development.

In this workshop, you'll start with a very simple Spring Boot application and learn to grow it into a fully functional application including a web front-end and data persistence. And you'll get hands-on experience with some of the most exciting new features in Spring 6 and Spring Boot 3.

Workshop Requirements

You'll need…

• Java (17 or higher)
• An IDE of your choosing (I'll be using either Spring Tools or IntelliJ, but VSCode or even NetBeans will be fine if you prefer)
• A web browser (who doesn't have one of those?)
• Docker Desktop (https://www.docker.com/products/docker-desktop/)

Although Java originally promised write once, run anywhere, it failed to fully deliver on that promise. As developers, we can develop, test, and build our applications into WAR or executable JAR files and then toss them over the wall to a Java application server and Java runtime that we have no control over, giving us zero confidence that the application will behave the same as when we tested it.

Containers fulfill the write-once, run anywhere promise that Java wasn't able to, by packaging the runtime and even the operating system along with our application, giving greater control and confidence that the application will function the same anywhere it is run. Additionally, containers afford several other benefits, including easy scaling, efficiency in terms of resource utilization, and security by isolating containers from their host system and from other containers.

While deploying Spring applications in containers has always been possible, Spring Boot makes it easier to containerize our applications and run them in container architectures such as Kubernetes. Spring Boot's support for containerization includes two options: Creating containers based on buildpacks or using layers as a means of modularizing and reducing the size of our application deployments. Moreover, new components in the Spring ecosystem can make your Spring applications Kubernetes-savvy so that they can take advantage of what a containerized architecture has to offer.

In this example-driven session, we're going to look at how to create and deploy Spring applications as container images and deploy them into a Kubernetes cluster. Along the way, we'll also get to know a few of the most useful tools that a Spring developer can employ in their development workflow when building containerized Spring applications. We'll also see how to apply patterns of Spring Cloud–such as configuration, service discovery, and gateways–using native Kubernetes facilities instead of Spring Cloud components. And we'll look at how components of the Spring ecosystem can work with your Spring applications to enable them to thrive in a Kubernetes cluster.

Workshop Requirements

• Java (17 or higher)
• An IDE of your choosing (I'll be using either Spring Tools or IntelliJ, but VSCode or even NetBeans will be fine if you prefer)
• Docker Desktop (https://www.docker.com/products/docker-desktop/)
• Kind (https://kind.sigs.k8s.io/)
• Kubectl (https://kubernetes.io/docs/reference/kubectl/)
• Skaffold (https://skaffold.dev/)

Test your setup: Make sure that the Docker Desktop is running and then type “kind create cluster”. It should take a minute or so, but then you should be able to type “kubectl config current-context” and see “kind-kind” listed.

At its core, Spring is built on a dependency injection framework that enables (among other things) loose coupling of application components. But dependency injection is not the only way to achieve loose-coupling. Event-driven design, as its name implies, enables components to react to events, not knowing where they came from, process those events, and then publish new events not knowing who (if anybody) will be handling those events.

In this session, we’re going to explore how to apply events in Spring applications to achieve looser coupling of components. We’ll start by looking at some fundamental abilities that the core framework offers for handling events. Then we'll quickly move into a higher-level form of event-driven architecture where events aren’t just handled within a single deployment, but span across multiple microservices by employing Spring Cloud Stream and Spring Cloud DataFlow. And then we’ll see how, when paired with serverless functions based on Spring Cloud Function, we can achieve not only loose-coupling of microservices, but also an efficient yet scalable use of resources in our event-driven architecture.

Contrary to popular belief, software engineering culture is not about the free food, open office spaces, and ping pong tables.

You can sense a great team culture when you observe it, and unfortunately you've probably experienced (or are currently experiencing) elements of a bad team culture. Not to worry - it's never too late to grab the reins and make small changes to steer your team in the right direction!

In this talk, Kate will highlight 8 tactical tips that attendees can implement on their teams immediately to help shape their team's culture!

Just as sharpening the saw is the best way to cut down a tree… sharpening your development environment allows for a more focused more productive experience.
This session is a collection of scripts, aliases, shells, editors and tools which will super charge your development experience.

This session will cover:

• zshell, oh my zsh and powerlevel10k
• system aliases, scripts and functions
• switching java, ruby and python environments
• git, hub and git aliases
• working pull-requests and stand-up meetings from the CLI
• iterm2 scripting and wizardry
• ngrok and working with secure call-backs

Bring your machine and lets have you productive within 2 hours!

Are you a Java Developer looking to work on a Golang project? Are you looking to get involve on cloud native projects such as Kubernetes? This session is for you! This session assumes are are a Java developer and details the nuances of Go with comparisons against Java-isms.

This session will take a deep dive into Go as a language and provide details necessary to understand and write idiomatic go applications. In addition to differences in how to use the language and packaging structures, we will look at options for standard idiomatic Java. This will include:

• Collection comparisons and what to use in Go
• Stream processing for filters and maps
• Packaging and encapsulation
• Error Handling (Go 1.13 style)
• Go formatting, style and linting

In the process, we will look at several Go projects in the Open Source space as style examples.

Machine Learning is all the rage, but many developers have no idea what it is, what they can expect from it or how to start to get into this huge and rapidly-changing field. The ideas draw from the fields of Artificial Intelligence, Numerical Analysis, Statistics and more. These days, you'll generally have to be a CUDA-wielding Python developer to boot. This workshop will gently introduce you to the ideas and tools, show you several working examples and help you build a plan to for diving deeper into this exciting new field.

We will cover:

• The differences between data science, AI and machine learning
• The Five Tribes of Machine Learning (as defined by Pedro Domingos)
• Walkthroughs of some of the main algorithms
• Examples in Java, R and Python
• Tools such as Tensorflow and Pytorch
• The impact of GPUs on machine learning
• Stories about how companies are being successful with machine learning
• A discussion about the likely impacts of machine learning on the job market and society

Workshop Requirements

Please install Anaconda for Python 3 before the workshop if possible. https://www.anaconda.com/download

Machine Learning is all the rage, but many developers have no idea what it is, what they can expect from it or how to start to get into this huge and rapidly-changing field. The ideas draw from the fields of Artificial Intelligence, Numerical Analysis, Statistics and more. These days, you'll generally have to be a CUDA-wielding Python developer to boot. This workshop will gently introduce you to the ideas and tools, show you several working examples and help you build a plan to for diving deeper into this exciting new field.

We will cover:

• The differences between data science, AI and machine learning
• The Five Tribes of Machine Learning (as defined by Pedro Domingos)
• Walkthroughs of some of the main algorithms
• Examples in Java, R and Python
• Tools such as Tensorflow and Pytorch
• The impact of GPUs on machine learning
• Stories about how companies are being successful with machine learning
• A discussion about the likely impacts of machine learning on the job market and society

Workshop Requirements

Please install Anaconda for Python 3 before the workshop if possible. https://www.anaconda.com/download

Our industry never stops changing, but sometimes those changes are trivia and fluffy. Sometimes they are fundamental and enduring. This series is going to highlight some of the most important trends happening in the hardware, software, data and architecture spaces.

Machine Learning is clearly here to stay. While it is a far cry from actual Artificial Intelligence, it provides many invaluable and remarkable ways to learn from the data we are collecting about our customers, products and daily activities. The past afforded us machine learning libraries which became machine learning frameworks. Now, we are designing and building machine learning platforms that facilitate entire initiatives in reusable and extensible ways. We will discuss many of the drivers of modern machine learning systems and the platforms that we are seeing emerge.

Modern software developers who want to learn from their data need to understand the machine learning landscape.

We will cover:

• The major languages and platforms involved in the machine learning space
• Different approaches to data science and machine learning for
• Individual analysts
• Backend developers
• API-oriented strategies for exposing Machine Learning capabilities
• An overview of the major platforms for training and deployment across a variety of hardware and software architectures