JHipster focuses on generating a high quality application with a Java back-end using an extensive set of Spring technologies; Spring Boot, Spring Security, Spring Data, Spring MVC (providing a framework for websockets, REST and MVC), etc. an Angular.js front-end and a suite of pre-configured development tools like Yeoman, Maven, Gradle, Grunt, Gulp.js and Bower. JHipster creates a fully configured Spring Boot application with a set of pre-defined screens for user management, monitoring, and logging. The generated Spring Boot application is specifically tailored to make working with Angular.js a smoother experience. Join Julien for a quick-live coding session to build a simple application, and deploy it to Cloud Foundry.
OK so everyone’s into big data but they’re usually talking about persistence, disk or more recently SSD, how about memory? We could simply add a few terabytes of RAM but even at $100 per GB that’s going to cost a LOT. What if we could reduce the size of the data by 50 fold and effectively bring the cost RAM down towards cost of disk? Keep Spring Integration, Spring Batch, GemFire in-memory cache, RabbitMQ messaging but reduce your data down to binary, yes bits and bytes rather than objects. Less garbage, less network overhead, same APIs but big-data in memory. John will show a Spring work-flow consuming 7.4kB XML messages, binding them to 25kB Java but storing them in just 450 bytes each, 10 million derivative contracts in-memory on a laptop.
For this session we will explore the power of Spring XD in the context of the Internet of Things (IoT). We will look at a solution developed with Spring XD to stream real time analytics from a moving car using open standards. Ingestion of the real time data (location, speed, engine diagnostics, etc), analyzing it to provide highly accurate MPG and vehicle range prediction, as well as providing real time dashboards will all be covered. Coming out of this session, you’ll understand how Spring XD can serve as “Legos®” for the IoT.
GEB (pronounced 'jeb') is a browser automation solution. It brings together the power of WebDriver, the elegance of jQuery content selection, the robustness of Page Object modelling and the expressiveness of the Groovy language.
Big Data. Fast Data. NoSQL. NewSQL. We've experienced something of a renaissance in the storage and processing of data in the last decade of computing after years of "Relational Winter." We're now entering into the next phase of this evolution: the convergence of data and the cloud. Much of this revolution has arrived on the coattails of data fabrics designed for horizontal scale-out on commodity hardware. Cloud platforms, especially PaaS platforms like Cloud Foundry, allow us to provision the requisite virtual hardware on-demand, removing the last mile of overhead in assembling scale-out data platforms. Coupling PaaS with microservice architectures and polyglot persistence allows developers to design systems utilizing stores uniquely designed for specific write, process, and query patterns. Leveraging the Lambda Architecture combination of real-time analytics platforms coupled with scale-out batch processing systems like Hadoop give us the ability to always ask questions of all of our data. In this talk will look at various Spring projects that allow us, coupled with Cloud Foundry, to uniquely position ourselves to take advantage of this convergence: Spring Boot: the opinionated framework for microservice development Spring Data: the access layer for SQL, NoSQL, NewSQL, and Hadoop Reactor: the foundation for reactive fast data applications on the JVM Spring XD: the platform for data ingest, real-time analytics, batch processing, and data export We'll tie all of these projects together in a suite of applications running on Cloud Foundry and Hadoop, closing the Apps/Data/Cloud loop.
Does your organization collect data? Lots of data? Does your organization make use of all that data they have collected? In this session you will learn what you do with machine learning, and what are the building blocks for an application that uses machine learning. This session will show you how to go from data you have collected to creating predictions for customers. You will learn how valuable insights into your data can be gleaned while building the code to make predictions.
The Amazon’s and Google’s of the world have had Ph.D.’s locked up in back rooms for years creating algorithms to get you to click on things and subsequently buy stuff. One of the big things that those smart people have been working on are recommendation engines. Today, a recommendation engine isn’t something that only the Amazon’s of the world can have. With an hour, and a handful of open source tools, we’ll build a recommendation engine based on the data from the website we probably spend the most time on…StackOverflow. We’ll use Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing (ingest, process, export) and use Apache Mahout to provide us with the recommendation processing. A basic understanding of Hadoop concepts (what Map/Reduce is) and Spring (basic D/I configuration) is expected for this talk.
An application designer usually has to choose where to trade flexibility for specificity (and thus usually performance); knowing when and where to do so is an art and requires experience. This talk will share over a decades worth of experience making these decisions and the learnings from developing Pivotal's successful Real Time Intelligence (RTI) product using the latest versions of Spring projects: Integration, Data, Boot, MVC/REST and XD. A walk through the RTI architecture will provide the base for an explanation about how Spring performs at hundreds (and millions) of events/operations per second and the techniques that you can use right now in your own Spring applications to minimise resource utilisation and gain performance.
Spring Cloud 1.0 is here! It offers a powerful way to create and consume microservices. As you introduce new services, you introduce integration problems: services can be shaky, they can disappear and - as they're often exposed over HTTP - they require a bit more footwork than in-process method invocations. In this webinar, we'll focus specifically on how Spring Cloud integrates service registration (e.g.: Eureka, Consul, or Zookeeper), declarative REST clients (with Netflix's Feign), reactive programming and the circuit breaker pattern with Hystrix to support easy, robust service-to-service invocations. This is a deep dive on how to make connect and consume microservices, and is a natural next step after my introduction to building microservices with Spring Cloud.
Tuesday, April 21st, 2015 2:00PM GMT (London GMT) Register
Tuesday, April 21st, 2015 10:00AM PDT (San Francisco GMT-07:00) Register
