In our previous post, we showed how to create an AWS Continuous Deployment Pipeline. This post will continue where we left off. We will enhance the pipeline with a Review stage, a more efficient use of the Maven cache and add notifications to the pipeline.Continue reading “How to Create an AWS Continuous Deployment Pipeline Cont’d”
Creating a continuous deployment pipeline will bring us a step closer to an automated build, test, deploy strategy. In order to create such a pipeline, we need to have access to several tools. Instead of installing these on on-premise servers, we can make use of the AWS cloud offer. Let’s see how this can be accomplished!Continue reading “How to Create an AWS Continuous Deployment Pipeline”
In this post, we will create a Spring Cloud Function and create some unit tests for it. We will do so by creating a function with Bean definition and with the functional style. At the end, we will deploy the function on AWS Lambda.Continue reading “How to Deploy a Spring Cloud Function on AWS Lambda”
In this post, we are going to explore how we can deploy a simple Spring Boot application to AWS Elastic Beanstalk. We will explain how to setup an AWS account and provide a step-by-step guide how to deploy to AWS.Continue reading “How to Deploy a Spring Boot App to AWS Elastic Beanstalk”
In this post, we take a look at how we can easily manage our database migration scripts by means of Liquibase. Liquibase will automatically execute necessary database migration scripts during application startup. We will explore some of the features of Liquibase by means of a simple Spring Boot application in combination with a PostgreSQL database.
Skaffold is a command line tool developed by Google which aims to facilitate continuous development for Kubernetes applications. It will automate the task of building and deploying to a Kubernetes cluster whereas you, as a developer, can stay focused on writing code. Seems interesting enough to take a closer look at it!
Some blog posts ago, we experimented with Kafka Messaging and Kafka Streams. Although we used Spring Boot applications in order to demonstrate some examples, we deliberately did not make use of Spring Kafka. Reason for doing so, was to get acquainted with Apache Kafka first without any abstraction layers in between. Now that we have done so, it is of course time to take a look at Spring Kafka!
In this post, we will take a closer look at Apache Kafka Messaging. We will show how you can easily start a Kafka cluster and how messages can be sent and received by means of CLI and from a Java application. At the end, we will explore how partitions work from a practical point of view.
In this post, we will take a look at how we can make services be aware of each other without knowing their exact location. We will make use of Eureka Server which will act as a Discovery Server. Being Spring fans, we will do so by means of Spring Eureka.
In this post, we will take a look at how we can use Google Cloud Vision from a Spring Boot application. With Google Cloud Vision it is possible to derive all kinds of things from images, like labels, face and text recognition, etc. As a bonus, some examples with Python are provided too.