Category: Containers

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 […]

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 […]

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 […]

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 […]

This post shows you how to configure CodeGuru Profiler to collect multiple applications’ profiling data into a central account and review the applications’ performance data on one dashboard.

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 […]

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.

This post was contributed by James Bland, Sr. Partner Solutions Architect, AWS, Jay Yeras, Head of Cloud and Cloud Native Solution Architecture, Snyk, and Venkat Subramanian, Group Product Manager, Bitbucket   One of our goals at Atlassian is to make the software delivery and development process easier. This post explains how you can set up […]