Publisher's Synopsis
1. Why Spring Cloud FunctionsThis chapter takes the reader through the need for Spring Cloud Functions and KNative. The rationale for Spring Cloud Functions will be elucidated through example implementation on on-prem and cloud infrastructures. The chapter highlights the "code once deploy anywhere" characteristic of Spring Cloud Functions. Subtitles1. Writing functions and deploying to any hyperscaler2. Example code3. Spring Cloud Functions on AWS (EKS, Fargate)4. Spring Cloud Functions on Azure (AKS)5. Spring Cloud Functions on Google (Cloud Run)6. Spring Cloud Functions on VMWare Tanzu (TKG, PKS)7. Spring Cloud Functions on RedHat OpenShift (OCP)
2. Getting Started with Spring Cloud FunctionsThis chapter walks the reader through the steps required to get started with Spring Serverless on their platform of choice, either locally, on-prem or on the cloud. Step by Step instructions take the reader through the process of getting the environment set up for coding.
Subtitles1. Step by Step: Setup locally with Kubernetes and KNative with Spring Cloud Functions2. Step by Step: Setup on AWS with EKS and KNative with Spring Cloud Functions3. Step by Step: Setup on GCP with Cloud Run/GKE and KNative with Spring Cloud Functions4. Step by Step: Setup on Azure with AKS and KNative with Spring Cloud Functions5. Step by Step: Setup on VMWare Tanzu TKG and KNative6. Step by Step: Setup on RedHat Openshift and KNative
3. Coding, testing, and deploying with Spring Cloud FunctionsThis chapter covers the coding, testing, and deploying using your favorite IDE like Eclipse, Eclipse Che, Intelij IDEA, Redhat Code Ready Workspace. The reader will build an example and deploy to their favorite platforma. Building a simple example with Spring Cloud Functionsb. Testing the example will sample datac. Setting up a CI/CD pipeline for deploying to a target platformd. Deploying to the target platfomi. AWSii. GCPiii. Azureiv. VMWare Tanzuv. RedHat Openshift
4. Building Event Driven Data pipelines with Spring Cloud FunctionsEvent Driven data pipelines act as a conduit to flow of data based on a specific event. The event can be a purchase order triggered on the website that initiate a data flow chain that includes aggregation of data from various data sources and splitting the data to various consumers. This chapter will discuss the various ways that Spring Spring Serverless can be implemented in the various Cloud providers
Subtitles1. Spring Cloud Functions and Spring Cloud Data Flow and Spring Cloud Streams2. Spring Cloud Functions and AWS Glue3. Spring Cloud Functions and Google Cloud Data Flow4. Spring Cloud Functions and Azure Data Factory
5. AI/ML Trained Serverless Endpoints with Spring Cloud FunctionsConversational AI models are one of the complex implementations that may lead to heavy use of resources in the cloud. Leveraging Serverless infra and functions can help alleviate the costs by being invoked only when needed. This chapter will help layout the blueprint of how to leverage Spring Serverless with on-prem or cloud-based AI/ML environments
Subtitles1. Spring Cloud Functions with Google Cloud Functions and Tensor Flow2. Spring Cloud Functions with AWS Glue and AWS Sage or AI/ML3. Spring Cloud Functions with Azure Data Factory and Azure ML4. Spring Cloud Functions with Apache AI/ML on-prem VMWare Tanzu and Openshift
6. Spring Cloud Functions and IOTThis chapter will
