Tag: AWS CodePipeline
Since the post Using AWS CodePipeline, AWS CodeBuild, and AWS Lambda for Serverless Automated UI Testing was published, things have evolved with Chrome headless and Firefox headless being supported natively. AWS Lambda now supports container images, AWS Step Functions has added support for Map state and its integration with Lambda, and AWS Fargate has enabled […]
AWS CodePipeline allows you to use a third-party Git repository as a source for a pipeline, however, the status of the build may not be available on the 3rd party git repository dashboard. As a developer, it is preferable to see the build / pipeline status in the same dashboard when working with repository. This […]
This post discusses how we can speed up the development of our Kubernetes infrastructure by using a continuous integration (CI) pipeline to build our Docker images and automatically deploy them to our Amazon Elastic Kubernetes Service (Amazon EKS) cluster using FluxCD and the GitOps philosophy as the continuous delivery (CD) element. To do so, we […]
Continuously building, testing, and deploying your web application helps you release new features sooner and with fewer bugs. In this blog, you will create a continuous integration and continuous delivery (CI/CD) pipeline for a web app using AWS CodeStar services and AWS Device Farm’s desktop browser testing service. AWS CodeStar is a suite of services […]
This post provides a step-by-step guide on how to model and provision AWS Glue workflows utilizing a DevOps principle known as infrastructure as code (IaC) that emphasizes the use of templates, source control, and automation. The cloud resources in this solution are defined within AWS CloudFormation templates and provisioned with automation features provided by AWS […]
We have seen mainframe customers often encounter scalability constraints, and they can’t support their development and test workforce to the scale required to support business requirements. These constraints can lead to delays, reduce product or feature releases, and make them unable to respond to market requirements. Furthermore, limits in capacity and scale often affect the quality of changes deployed, and are linked to unplanned or unexpected downtime in products or services.
The conventional approach to address these constraints is to scale up, meaning to increase MIPS/MSU capacity of the mainframe hardware available for development and testing. The cost of this approach, however, is excessively high, and to ensure time to market, you may reject this approach at the expense of quality and functionality. If you’re wrestling with these challenges, this post is written specifically for you.
Managing applications deployments on Raspberry Pi can be cumbersome, especially in headless mode and at scale when placing the devices outdoors and out of reach such as in home automation projects, in the yard (for motion detection) or on the roof (as a humidity and temperature sensor). In these use cases, you have to remotely […]
As companies implement DevOps practices, standardizing the deployment of continuous integration and continuous deployment (CI/CD) pipelines is increasingly important. Your developer team may not have the ability or time to create your own CI/CD pipelines and processes from scratch for each new project. Additionally, creating a standardized DevOps process can help your entire company ensure […]
September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. This is a guest post from Pushly. In their own words, “Pushly provides a scalable, easy-to-use platform designed to deliver targeted and timely content via web push notifications across all modern desktop browsers and Android devices.” Introduction As a software engineer at […]
Researchers at Academic Medical Centers (AMCs) use programs such as Observational Health Data Sciences and Informatics (OHDSI) and Research Electronic Data Capture (REDCap) to interact with healthcare data. Our internal team at AWS has provided solutions such as OHDSI-on-AWS and REDCap environments on AWS to help clinicians analyze healthcare data in the AWS Cloud. Occasionally, […]