Category: Learning Levels

Observability in a container-centric environment presents new challenges for operators due to the increasing number of abstractions and supporting infrastructure. In many cases, organizations can have hundreds of clusters and thousands of services/tasks/pods running concurrently. This post will demonstrate new features in Amazon DevOps Guru to help simplify and expand the capabilities of the operator. […]

Amazon DevOps Guru is a fully managed service that uses machine learning (ML) to continuously analyze and consolidate operational data streams from multiple sources, such as Amazon CloudWatch metrics, AWS Config, AWS CloudFormation, AWS X-Ray, and provide you with a single console dashboard. This dashboard helps customers improve operational performance and avoid expensive downtime by […]

Applying various transformations to images at scale is an easily parallelized and scaled task. As a Computer Vision research team at Amazon, we occasionally find that the amount of image data we are dealing with can’t be effectively computed on a single machine, but also isn’t large enough to justify running a large and potentially […]

Critical business applications are monitored in order to prevent anomalies from negatively impacting their operational performance and availability. Amazon DevOps Guru is a Machine Learning (ML) powered solution that aids operations by detecting anomalous behavior and providing insights and recommendations for how to address the root cause before it impacts the customer. This post demonstrates how Amazon […]

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 […]

Software as a Service (SaaS) is an increasingly popular business model for independent software vendors (ISVs), including benefits such as a pay-as-you-go pricing model, scalability, and availability. SaaS services can be built by using numerous architectural models. The silo model provides each tenant with dedicated resources and a shared-nothing architecture. Silo deployments also provide isolation […]

Organizations implement compliance rules for cloud infrastructure to ensure that they run the applications according to their best practices. They utilize AWS Config to determine overall compliance against the configurations specified in their internal guidelines. This is determined after the creation of cloud resources in their AWS account. This post will demonstrate how to use […]

This blog post will demonstrate how to leverage Jenkins with Amazon Elastic Kubernetes Service (EKS) by running a Jenkins Manager within an EKS pod. In doing so, we can run Jenkins workloads by allowing Amazon EKS to spawn dynamic Jenkins Agent(s) in order to perform application and infrastructure deployment. Traditionally, customers will setup a Jenkins Manager-Agent […]

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 […]

Microservices commonly communicate with JSON over HTTP/1.1. These technologies are ubiquitous and human-readable, but they aren’t optimized for communication between dozens or hundreds of microservices. Next-generation Web technologies, including gRPC and HTTP/2, significantly improve communication speed and efficiency between microservices. AWS offers the most complete platform for builders implementing microservices — and the addition of HTTP/2 and gRPC support in Application Load Balancer (ALB) provides an end-to-end solution for next-generation microservices. ALBs can inspect and route gRPC calls, enabling features like health checks, access logs, and gRPC-specific metrics. This post demonstrates .NET microservices communicating with gRPC via Application Load Balancers.