Transporting Data at Warp Speed: How to Connect Spring Boot Apps Quickly, Powerfully, and Painlessly

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SpringOne Platform 2017 Thomas Risberg, Pivotal In this talk we will give an overview of the challenges involved in deploying a Spring Boot app on Kubernetes. How do you deploy the web app and a database together? How do you configure your app with the database password? We will take a look at what's needed to deploy Spring Cloud Data Flow server on Kubernetes, both for testing and for a real production deployment. We'll also discuss using Helm for app deployments.

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Learn how enterprise leaders are using Azure Spring Cloud to transform their IT operations and deliver value. This moderated panel discussion will feature customers sharing real-world stories about: • Running Spring apps in the cloud at enterprise scale • Embracing hybrid as the new normal • Transforming their technology stacks • Implementing zero-trust security and network requirements • Empowering their developers to rapidly dev and deploy • Delivering value faster to their end customers

Accelerate Spring Apps to Cloud at Scale

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Ever wanted to be a fly on the wall in someone else's retrospective? Now you can! Join Jon Mort and Adam Markham from Adaptavist as they walk us through recent lessons learned from their team. Hear how ScriptRunner, Adaptavist's popular Automation add-on moved into the cloud. Learn about the parts they loved as well as challenges and frustrations they faced. This honest and open talk will be easy for any add-on developer to relate to. Jon Mort, Head of Engineering Research and Development, Adaptavist Adam Markham, Software Developer, Adaptavist

Bringing Server Add-ons to the Cloud and Back Again

Atlassian

Spring Boot Loves K8s

The goal of every developer is get her super cool new feature out to customers, as fast as possible, with little to no bugs and with no manual effort so she can go back to coding the next awesome one. Doing all of this takes tremendous amounts of effort to plan, coordinate and execute on a DevOps engineer. Continuous Integration coupled with Continuous Deployment aide in this endeavor. But again, those are cumbersome and can be difficult to set up. AWS has four new tools to help with this; AWS CodeDeploy, CodeCommit, CodePipeline, and CodeBuild. Each one has specialized features to help get your code to customers faster, more reliable and bug free as possible. In this presentation, we will walk through how to setup a CI/CD pipeline using those AWS tools and demonstrate how we can go from yay it compiles to a 5-star review.

DevOps and AWS - Code PaLOUsa 2017

James Strong

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Davide Benvegnù

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How does one choose to architect a system that has a Microservice / REST API endpoints? There are many solutions out there. Some are better than others. Should state be held in a server side component, or externally? Generally we are told this is not a good practice for a Cloud Native system, when the 12-factor guidelines seem to be all about stateless containers, but is it? It’s unclear and this confusion may lead to poor technology stack choices that are impossible or extremely hard to change later on as your system evolves in terms of demand and performance. While stateless systems are easier to work with, the reality is that we live in a stateful world, so we have to handle the state of data accordingly to ensure data integrity beyond securing it. We will examine and demonstrate the fundamentals of a Cloud Native system with Stateful Microservices that’s built with Open Liberty and MicroProfile in Kubernetes.

Was ist angesagt? (20)

Supercharge Your Spring Boot Apps!

Taking Spring Apps for a Spin on Microsoft Azure Cloud

“Sh*^%# on Fire, Yo!”: A True Story Inspired by Real Events

Deploying Spring Boot apps on Kubernetes

Infrastructure as Code (BBWorld/DevCon13)

Handling Secrets in Your Cloud Native Architecture

Not Just Initializing

Going Serverless Using the Spring Framework Ecosystem

What Is Spring?

Spring Tools 4: Bootiful Spring Tooling for the Masses

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Accelerate Spring Apps to Cloud at Scale

Introduction To Jenkins - SpringPeople

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Spring Boot Loves K8s

DevOps and AWS - Code PaLOUsa 2017

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Microsoft Skills Bootcamp - The power of GitHub and Azure

An Integrated Pipeline for Private and Public Clouds with Jenkins, Artifactor...

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Developing a mobile app can be time-consuming and frustrating -- writing server-side code, handling and scaling servers, designing APIs, all the while designing exceptional user experience. This presentation deck will help you understand the basics of serverless app development, and how it’ll save you and your team time and reduce development overhead. Check out our open source serverless framework Skygear.io Questions? Ask us anything at hello@skygear.io

Why BaaS is crucial to early stage startups

Jane Chung

As your company accumulates more data, it’s important to leverage all of it to develop new advanced machine learning models. And now, you can scale Spark using Kubernetes. Thanks to the new native integration between Apache Spark’s and Kubernetes, scaling data processing has never been easier. Apache Spark is a well designed high level application that can increase your data processing speed and accuracy. It can handle batch and real-time analytic and data processing workloads. This high level and efficient technology can be used with Java/Spark/Python and R. Joined with Kubernetes, you can get twice the efficiency. Kubernetes is a great engine with the most popular framework for managing compute resources. Unfortunately, running Apache Spark on Kubernetes can be a pain for first-time users. Join CTO of cnvrg.io Leah Kolben as she brings you through a step by step tutorial on how to run Spark on Kubernetes. You’ll have your Spark up and running on Kubernetes in just 30 minutes. Running Spark on Kubernetes will help you: Process larger amounts of data Segment your data into sub groups Watch all our webinars at https://cnvrg.io/webinars-and-workshops/

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Today’s cutting-edge companies have software release cycles measured in days instead of months. This agility is enabled by the DevOps practice of continuous delivery, which automates building, testing, and deploying all code changes. This automation helps you catch bugs sooner and accelerates developer productivity. In this session, we’ll share the processes that Amazon’s engineers use to practice DevOps and discuss how you can bring these processes to your company by using a new set of AWS tools (AWS CodePipeline and AWS CodeDeploy). These services were inspired by Amazon's own internal developer tools and DevOps culture.

DevOps on AWS: Deep Dive on Continuous Delivery and the AWS Developer Tools

Amazon Web Services

ACCS provides the perfect cloud service to develop microservices on! In this presentation I'll demonstrate some of recent the highlights for developers such as Python support, integration options with the Event Hub and I'll go into detail for using the Application Caches to increase performance. Spring Boot will be used extensively in this presentation. After this presentation you will have a better understanding of the options provided by ACCS to create microservices quick and easily.

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In a world of serverless computing users tend to be frugal when it comes to expenditure on compute, storage and other resources. Paying for the same when they aren’t in use becomes a significant factor. Offering Spark as service on cloud presents very unique challenges. Running Spark on Kubernetes presents a lot of challenges especially around storage and persistence. Spark workloads have very unique requirements of Storage for intermediate data, long time persistence, Share file system and requirements become very tight when it same need to be offered as a service for enterprise to mange GDPR and other compliance like ISO 27001 and HIPAA certifications. This talk covers challenges involved in providing Serverless Spark Clusters share the specific issues one can encounter when running large Kubernetes clusters in production especially covering the scenarios related to persistence. This talk will help people using Kubernetes or docker runtime in production and help them understand various storage options available and which is more suitable for running Spark workloads on Kubernetes and what more can be done

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For a long time I have been reluctant to actively contribute to an open source project. I thought it would be rather complicated and demanding – and that I didn't have the knowledge or skills for it or at the very least that they (the project team) weren't waiting for me. In December 2021, I decided to have a serious input into the Dapr.io project – and now finally to determine how it works and whether it is really that complicated. In this session I want to tell you about my experiences. How Fork, Clone, Branch, Push (and PR) is the rhythm of contributing to an open source project and how you do that (these are all Git actions against GitHub repositories). How to learn how such a project functions and how to connect to it; which tools are needed, which communication channels are used. I tell how the standards of the project – largely automatically enforced – help me to become a better software engineer, with an eye for readability and testability of the code. How the review process is quite exciting once you have offered your contribution. And how the final "merge to master" of my contribution and then the actual release (Dapr 1.6 contains my first contribution) are nice milestones. I hope to motivate participants in this session to also take the step yourself and contribute to an open source project in the form of issues or samples, documentation or code. It's valuable to the community and the specific project and I think it's definitely a valuable experience for the "contributer". I looked up to it and now that I've done it gives me confidence – and it tastes like more (I could still use some help with the work on Dapr.io, by the way).

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As developers, we hear a non-stop stream of technical-but-marketing messages for containers, orchestration tools, and cloud services. There is extensive overlap in these areas with regard to both means and ends, and it's time to clear the fog and get to the bottom of things. This talk gives a quick overview from a hardcore developer's perspective of the following topics: * How can I use containers to develop better software? * What are orchestration tools? Do I need to consider/use them? * How do cloud/PaaS options compare? What are the tradeoffs? * What is the difference? * Why should I care? (Or should I?) In this session, the presenter discusses several of these technologies, compares them, and deploys real applications to them LIVE to demonstrate subtle differences and tradeoffs each choice imposes upon developers, for better or worse.

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As developers, we hear a non-stop stream of technical-but-marketing messages for containers, orchestration tools, and cloud services. There is extensive overlap in these areas with regard to both means and ends, and it's time to clear the fog and get to the bottom of things. The talk that accompanies these slides gives a quick overview from a hardcore developer's perspective of the following topics: * How can I use containers to develop better software? * What are orchestration tools? Do I need to consider/use them? * How do cloud/PaaS options compare? What are the tradeoffs? * What is the difference? * Why should I care? (Or should I?) In the accompanying session, the presenter discusses several of these technologies, compares them, and deploys real applications to them LIVE to demonstrate subtle differences and tradeoffs each choice imposes upon developers, for better or worse.

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