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SpringOne2GX 2014 Replay: IoT Realized - The Connected Car

Recorded at SpringOne2GX 2014.

Speakers: Derek Beauregard, Phil Berman, Michael Minella, Darrel Sharpe

Big Data Track Slides: http://www.slideshare.net/SpringCentral/iot-realized-the-connected-car

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.

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SpringOne2GX 2014 Replay: Big Data in Memory

Recorded at SpringOne2GX 2014. Big Data in Memory

Speaker: John Davies, C24

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014-john-davies-41130273

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.

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SpringOne2GX 2014 Replay: Asychronous design with Spring and RTI: 1M events per second

Recorded at SpringOne2GX 2014.

Speaker: Stuart Williams

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/williams-1m-events

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.

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SpringOne2GX 2014 Replay: Building a Recommendation Engine with Spring and Hadoop

Recorded at SpringOne2GX 2014.

Speaker: Michael Minella

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/building-a-recommendation-engine-with-spring-and-hadoop

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.

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SpringOne2GX 2014 Replay: Become a data-driven Organization with Machine Learning

Recorded at SpringOne2GX 2014.

Speaker: Peter Harrington

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014peterharrington

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.

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SpringOne2GX 2014 Replay: Apps + Data + Cloud: What Does It All Mean?

Recorded at SpringOne2GX 2014.

Speaker: Matt Stine

Developing for the Cloud Track

Slides: http://www.slideshare.net/SpringCentral/apps-data-cloud-what-does-it-all-mean

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.

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Webinar: Reactive data-pipelines with Spring XD and Kafka

Speakers: Marius Bogoevici & Mark Pollack

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.

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Webinar: Smarter Service-to-Service Invocations with Spring Cloud

Speaker: Josh Long

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.

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