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Webinar: Data Microservices with Spring Cloud Data Flow

Speakers: Mark Fisher & Mark Pollack, Pivotal
The future of scalable data processing is microservices! Building on the ease of development and deployment provided by Spring Boot and the cloud native capabilities of Spring Cloud, the Spring Cloud Stream and Spring Cloud Task projects provide a simple and powerful framework for microservice stream and batch processing.
At a higher level of abstraction, Spring Cloud Data Flow is an integrated orchestration layer that provides a highly productive experience for deploying and managing sophisticated data pipelines consisting of standalone microservices. Streams and tasks are defined using a DSL abstraction and can be managed via shell and a web UI. Furthermore, a pluggable runtime SPI allows Spring Cloud Data Flow to coordinate these applications across a variety of distributed runtime platforms such as Cloud Foundry, Apache Mesos and Apache YARN.
During this webinar you’ll see an overview of Spring Cloud Data Flow, with live demos of streaming and batch apps, on different platforms ranging from local cluster to a remote Cloud to show the simplicity of the developer experience.

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Webinar: Introducing Spring Cloud Task

Speaker: Michael Minella, Pivotal

One of the major promises of the cloud is that of flexibility. Today, most applications deployed to the cloud are long running processes that use the flexibility of cloud scaling. But computing is full of short lived tasks that start up, do their work, and then terminate. These tasks are excellent cloud use cases since resources can quickly be provisioned - and reclaimed.

In this webinar, we’ll explore a new project in the Spring Cloud portfolio, Spring Cloud Task, a new framework for developing and orchestrating short-lived microservices. We’ll explore various use cases, build your first task, and discuss how to orchestrate tasks using various techniques. Finally, we’ll peek into the roadmap for the Spring Cloud Task project.

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SpringOne2GX 2015 replay: NoXML - Eliminating XML in Your Spring Projects

Recorded at SpringOne2GX 2015.
Speaker: Matt Raible
Core Spring track
Slides: http://www.slideshare.net/SpringCentral/noxml-eliminating-xml-in-your-spring-projects

Many Spring projects exist that leverage XML for their configuration and bean definitions. Most Java web applications use a web.xml to configure their servlets, filters and listeners. This session shows you how you can eliminate XML by configuring your Spring beans with JavaConfig and annotations. It also shows how you can remove your web.xml and configure your web components with Java.

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SpringOne2GX 2015 replay: Microservices to FastData in the Enterprise with Spring

Recorded at SpringOne2GX 2015.
Speaker: John T Davies
Big Data track
Slides: http://www.slideshare.net/SpringCentral/microservices-to-fastdata-in-the-enterprise-with-spring

How do you deal with massive volumes of data, hundreds of thousands per second? Just chuck it into RAM but what about GC? If we scale to terabytes we need to distribute, what about serialisation costs? How do micro services fit in? In this talk we’ll be using Spring Integration and Spring Boot to ingest gigabytes of complex data into an two different in memory data grids (IMDGs). Based on applications running in production this talk will walk through the design and implementation with several demos.

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SpringOne2GX 2015 replay: High Performance Stream Processing

Recorded at SpringOne2GX 2015.
Speaker: Stephane Maldini, Glenn Renfo, David Turanski
Big Data track
Slides: http://www.slideshare.net/SpringCentral/high-performance-stream-processing-53163754

Making things go fast is harder than it looks. In this talk we dissect a Spring XD application into three areas - message transport, data serialization, and application processing - to show design patterns and techniques that will help you get the highest throughput and lowest resource utilization for your streaming applications. We will compare and contrast in-memory, RabbitMQ, and Kafka transports; Kryo, Protocol Buffers and C24 Simple Data Objects serialization libraries; and processing patterns that use a traditional shared queue and thread pool dispatcher in contrast to a Reactive Streams approach.

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This Week in Spring - April 5th, 2016

Welcome to another installment of This Week in Spring! As usual, we’ve got a lot to cover so let’s get to it!

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SpringOne2GX 2015 replay: Reactive Web Applications

Recorded at SpringOne2GX 2015.
Speaker: Stephane Maldini, Rossen Stoyanchev
Web / Javascript track
Slides: http://www.slideshare.net/SpringCentral/reactive-web-applications-53170985
In our previous talk “Intro to Reactive Programming” we defined reactive programming and provided details around key initiatives such as Reactive Streams and ReactiveX. In this talk we’ll focus on where we are today with building reactive web applications. We’ll take a look at the choice of runtimes, how Reactive Streams may be applied to network I/O, and what the programming model may look like. While this is a forward looking talk, we’ll spend plenty of time demoing code built with with back-pressure ready libraries available today.

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SpringOne2GX 2015 replay: Spring Data Daily Double - Couchbase and Neo4J

Recorded at SpringOne2GX 2015.
Speakers: Michael Wilmes, Lufthansa and Laurent Doguin, Couchbase
Data / Integration Track
http://www.slideshare.net/SpringCentral/inflight-asset-management-with-spring-data-and-neo4j
We invite you to join these two speakers from the Spring Data community, each speaking about their respective projects for 45 minutes.
Michael Wilmes from Lufthansa Systems will speak about Graph-based Asset Management with the Spring Framework. He is part of a flagship project that brings next-generation Inflight-Entertainment systems onto passenger aircrafts, and tackles the operational aspects of managing IT systems in flight, on ground and anywhere in between. Within his talk he will give insight on how Lufthansa Systems leverages the Spring Stack for solving an asset management problem scope with Spring Data and Neo4j.
Next, Laurent Doguin from Couchbase will tell us how to “Manage Time-base data with Spring Data Couchbase”. A common thing to do in the big data world is store time-based data. It can be logs, user events, social media metrics, market data indicators or even more common now sensor data. You can do that very easily with Spring Data Couchbase. I this talk I will tell you what you need to know before starting to store time-based data. I’ll talk about use cases, data modeling, how Couchbase is a perfect fit for this kind of job and code a little demo that reflects all of this.

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Webinar Replay: Up and Running with Spring Boot in NetBeans IDE

Guest Speaker: Geertjan Wielenga
Slides: no slides all demo
NetBeans IDE is the official IDE of the Java platform. Focused on rapid out-of-the-box development of all kinds of Java applications, NetBeans is comprehensive and easy to use. In particular, its free and open source tooling for end-to-end development is much admired.
Over recent years, many developers have added NetBeans to their toolbox because of its intuitive and powerful integration with Maven and Gradle. During this session you will see how NetBeans is well suited for setting up and developing applications that make use of Spring Boot.
You will also learn about the many new features that have been added in NetBeans IDE 8.0 and 8.1, helping you working quickly and effectively with Java and Maven in the context of Spring Boot. By the end of this session, you’ll have a better grasp of Spring Boot, as well as a thorough understanding of the range of features that NetBeans provides for working with it!

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SpringOne2GX 2015 replay: Developing Real-Time Data Pipelines with Apache Kafka

Recorded at SpringOne2GX 2015.
Speaker: Joe Stein
Big Data Track
Slides: http://www.slideshare.net/SpringCentral/developing-realtime-data-pipelines-with-apache-kafka-53201942
Developing Real-Time Data Pipelines with Apache Kafka http://kafka.apache.org/ is an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log. Kafka is designed to allow a single cluster to serve as the central data backbone. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of coordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages. For the Spring user, Spring Integration Kafka and Spring XD provide integration with Apache Kafka.

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