AWS DevOps & Developer Productivity Blog

Category: Containers

Deploy container applications in a multicloud environment using Amazon CodeCatalyst

Deploy container applications in a multicloud environment using Amazon CodeCatalyst

In the previous post of this blog series, we saw how organizations can deploy workloads to virtual machines (VMs) in a hybrid and multicloud environment. This post shows how organizations can address the requirement of deploying containers, and containerized applications to hybrid and multicloud platforms using Amazon CodeCatalyst. CodeCatalyst is an integrated DevOps service which […]

How organizations are modernizing for cloud operations

Over the past decade, we’ve seen a rapid evolution in how IT operations teams and application developers work together. In the early days, there was a clear division of responsibilities between the two teams, with one team focused on providing and maintaining the servers and various components (i.e., storage, DNS, networking, etc.) for the application […]

Journey to adopt Cloud-Native DevOps platform Series #1: OfferUp modernized DevOps platform with Amazon EKS and Flagger to accelerate time to market

In this two part series, we discuss the challenges faced by OfferUp, a Digital Native customer, to meet business growth and time-to-market. Their journey involved modernizing their existing DevOps platform, from the traditional monolith virtual machine (VM) based architecture to modern containerized architecture and running cloud-native applications for secured progressive delivery to accelerate time to […]

Generate DevOps Guru Proactive Insights in ECS using Container Insights

Generating DevOps Guru Proactive Insights for Amazon ECS

Monitoring is fundamental to operating an application in production, since we can only operate what we can measure and alert on. As an application evolves, or the environment grows more complex, it becomes increasingly challenging to maintain monitoring thresholds for each component, and to validate that they’re still set to an effective value. We not […]

Simulated location data with Amazon Location Service

Modern location-based applications require the processing and storage of real-world assets in real-time. The recent release of Amazon Location Service and its Tracker feature makes it possible to quickly and easily build these applications on the AWS platform. Tracking real-world assets is important, but at some point when working with Location Services you will need to […]

Use Amazon ECS Fargate Spot with CircleCI to deploy and manage applications in a cost-effective way

This post is written by Pritam Pal, Sr EC2 Spot Specialist SA & Dan Kelly, Sr EC2 Spot GTM Specialist Customers are using Amazon Web Services (AWS) to build CI/CD pipelines and follow DevOps best practices in order to deliver products rapidly and reliably. AWS services simplify infrastructure provisioning and management, application code deployment, software […]

Lambda-docker-images-DevOps-Architecture

Using AWS CodePipeline for deploying container images to AWS Lambda Functions

AWS Lambda launched support for packaging and deploying functions as container images at re:Invent 2020. In the post working with Lambda layers and extensions in container images, we demonstrated packaging Lambda Functions with layers while using container images. This post will teach you to use AWS CodePipeline to deploy docker images for microservices architecture involving […]

Build and deploy .NET web applications to ARM-powered AWS Graviton 2 Amazon ECS Clusters using AWS CDK

With .NET providing first-class support for ARM architecture, running .NET applications on an AWS Graviton processor provides you with more choices to help optimize performance and cost. We have already written about .NET 5 with Graviton benchmarks; in this post, we explore how C#/.NET developers can take advantages of Graviton processors and obtain this performance […]

Mainfrme DevOps On AWS Architecture Overview, Two types of pipelines, Project Pipeline and Regression Pipeline

Automate thousands of mainframe tests on AWS with the Micro Focus Enterprise Suite

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.