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.
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.
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.
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.
In the recent years, drastic increases in data volume as well as a greater demand for low latency have led to a radical shift in business requirements and application development methods. In response to these demands, frameworks such as RxJava and high throughput messaging systems such as Kafka have emerged as key building blocks.
However, integrating technologies is never easy and Spring XD provides a solution. Through its development model and runtime, Spring XD makes it easy to develop highly scalable data pipelines, and lets you focus on writing and testing business logic vs. integrating and scaling a big data stack.
Come and see how easy this can be in this webinar, where we will demonstrate how to build highly scalable data pipelines with RxJava and Kafka, using Spring XD as a platform.
Tuesday, April 28th, 2015 2:00PM GMT (London GMT) Register
Tuesday, April 28th, 2015 10:00AM PDT (San Francisco GMT-07:00) Register
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
Spring Security, Spring Boot and Angular JS all have nice features for making it really easy to produce modern applications, so there is potentially a lot of value in making them work together very smoothly. Things to consider are cookies, headers, native clients, various security vulnerabilities and how modern browser technology can help us to avoid them. In this session we show how nice features of the component frameworks can be integrated simply to provide a pleasant and secure user experience. We start with a very basic single-server implementation and scale it up in stages, splitting out backend resources and authentication to separate services. The final state includes a simple API Gateway on the front end implemented declaratively using Spring Cloud, and using this we are able to neatly sidestep a lot of the problems people encounter securing a javascript front end with a distributed back end.
Back in the era of the application server, HTTP Session replication was a common way to scale out user session data, as well as make it fault tolerant. In today's world of lightweight containers, PaaS, and virtualization, Spring Session offers a 100% server and/or container portable HTTP Session. See how Spring Session easily plugs in implementations like Redis, scales out across a cluster, handles multiple users' browser sessions, works with WebSocket, and allows header based authentication within REST apps. It's a perfect fit for working with user data, particularly in a highly distributed environment like the cloud, or on a Platform like Cloud Foundry.
Whether you have one or a million visitors accessing your Spring web app, they are all going to demand a great user experience regardless of what it takes for you to deliver it. This invariably means quick page loads and fast response times every single time. When things go south, you just throw more hardware at the problem and increase your caches and buffers, right? Wrong. Toss in an infrastructure that resides on the cloud and now you’ve got a really interesting problem on your hands. I’ll leave the marketecure slides at the door, this is a hands-on technical talk in which we’ll deploy an application to the cloud and then turn up the heat by leveraging the right mix of elasticity and auto-scaling.
Developing an application to a cloud platform involves working with deployed application's environment and connecting to services. Spring Cloud, a new project, simplifies these tasks in a variety of cloud platforms including Cloud Foundry and Heroku. Spring Cloud makes it possible to deploy the same artifact (a war or a jar) to multiple cloud environments. It supports multiple clouds through the concept of Cloud Connector and provides out of the box implementation for Cloud Foundry and Heroku. Spring Cloud is designed for extension, making it simple to create a cloud connector for other cloud platforms. Spring Cloud also supports connecting to multiple services through the concept of service connectors. Out of the box, it provides support for many common services, but also makes it easy to extend it to other services. While Spring Cloud can be used by applications using any JVM language and framework, it further simplifies Spring applications through Java and XML-based configuration. In this talk, we will introduce the Spring Cloud project, show how you can simplify configuring applications for cloud deployment, discuss its extensibility mechanism, and put it to good use by showing practical examples from the field.
