AWS DevOps & Developer Productivity Blog

Artifact repositories are often used to share software packages for use in builds and deployments. Java developers using Apache Maven use artifact repositories to share and reuse Maven packages. For example, one team might own a web service framework that is used by multiple other teams to build their own services. The framework team can […]

As applications become increasingly distributed and complex, operators need more automated practices to maintain application availability and reduce the time and effort spent on detecting, debugging, and resolving operational issues. Enter Amazon DevOps Guru (preview). Amazon DevOps Guru is a machine learning (ML) powered service that gives you a simpler way to improve an application’s […]

We are pleased to announce the launch of Python support for Amazon CodeGuru, a service for automated code reviews and application performance recommendations. CodeGuru is powered by program analysis and machine learning, and trained on best practices and hard-learned lessons across millions of code reviews and thousands of applications profiled on open-source projects and internally […]

Amazon CodeGuru is a developer tool that provides intelligent recommendations for improving code quality and identifies an application’s most expensive lines of code. To help you find and remediate potential security issues in your code, Amazon CodeGuru Reviewer now includes an expanded set of security detectors.  In this post, we discuss the new types of […]

Managing NuGet packages for .NET development can be a challenge. Tasks such as initial configuration, ongoing maintenance, and scaling inefficiencies are the biggest pain points for developers and organizations. With its addition of NuGet package support, AWS CodeArtifact now provides easy-to-configure and scalable package management for .NET developers. You can use NuGet packages stored in […]

Learn how to create, publish, and download private npm packages using AWS CodeArtifact, allowing you to share code across your organization without exposing your packages to the public.

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 […]