To date, organizations have adopted PCF because it acts as an abstraction layer on public and private cloud IaaS providers, increasing business agility and time-to-market of software products.
This course kicks off by showing you the steps involved in making your application cloud-ready. Moving on, it delves into the various aspects involved in deploying your application and running it on Cloud Foundry. It also shows you how Spring Boot and PCF tremendously increase developer productivity.
Later, we address the core challenges that Java developers face while developing PCF applications, such as understanding what PCF is and why companies adopt it. Then, it shows you how to quickly bootstrap and deploy various micro-services to PCF, including REST web services and event-driven applications.
At the end of this video course, you’ll learn how to set up your work space, and its architecture, develop a web service application, and deploy it to PCF. You’ll also build a micro-service (event-driven) and deploy it to Cloud Foundry.
About the Author
Braithe E.S. Warnock is currently a managing cloud architect for the financial services division of Ernst & Young. He has had the opportunity to work with several of the largest PCF installations on an international scale. He has helped build the framework for the adoption of PCF at top companies such as Ford, Comcast, DISH, HSBC, and Charles Schwab. As a vendor-neutral consultant, Braithe enjoys helping people understand the rapidly-evolving world of cloud and application architecture.
Braithe has more than 6 years’ experience and a specialization in global digital transformations. He has expertise in various cloud and cloud platform technologies (PCF, AWS, Azure, VMware, Netflix OSS, Kubernetes, and OpenShift), and also the Java and Spring Boot frameworks. He has developed over 100 micro-services using Spring Boot, Java 7/8, Spring Cloud, and Netflix OSS, spanning across half a dozen unique cloud-native micro-service architectures. He also has experience in developing machine learning models using AWS, Spark, and MLlib to support product recommendations and enhance customer data.
