AWS Architecture Blog

Architecture for wind turbine digital twin solution

Physics on AWS: Optimizing wind turbine performance using OpenFAST in a digital twin

Wind energy plays a crucial role in global decarbonization efforts by generating emission-free power from an abundant resource. In 2022, wind energy produced 2100 terawatt-hours (TWh) globally, or over 7% of global electricity, with expectations to reach 7400 TWh by 2030. Despite its potential, several challenges must be addressed to help meet grid decarbonization targets. […]

Infrastructure for the chaos experiment

Behavior Driven Chaos with AWS Fault Injection Simulator

A common challenge organizations face is how to gain confidence in and provide evidence for the continuous resilience of their workloads. Using modern chaos engineering principles can help in meeting this challenge, but the practice of chaos engineering can become complex. As a result, both the definition of the inputs and comprehension of the outputs […]

End-to-end solution using Zurich's tooling

How Zurich Insurance Group built their Scalable Account Vending process using AWS Account Factory for Terraform

Introduction Zurich Insurance Group is a leading multi-line global insurer operating in more than 200 territories. Headquartered in Zurich, Switzerland, their main business is life and property and casualty (P&C) insurance. In 2022, Zurich began a multi-year program to accelerate their digital transformation and innovation through migration of 1,000 workloads to AWS, including core insurance […]

Unified API architecture

How Sonar built a unified API on AWS

SonarCloud, a software-as-a-service (SaaS) product developed by Sonar, seamlessly integrates into developers’ CI/CD workflows to increase code quality and identify vulnerabilities. Over the last few months, Sonar’s cloud engineers have worked on modernizing SonarCloud to increase the lead time to production. Following Domain Driven Design principles, Sonar split the application into multiple business domains, each […]

Example of a stateless architecture

Converting stateful application to stateless using AWS services

Designing a system to be either stateful or stateless is an important choice with tradeoffs regarding its performance and scalability. In a stateful system, data from one session is carried over to the next. A stateless system doesn’t preserve data between sessions and depends on external entities such as databases or cache to manage state. […]

Let's Architect

Let’s Architect! Tools for developers

In the software development process, adopting developer tools makes it easier for developers to write code, build applications, and test more efficiently. As a developer, you can use various AWS developer tools for code editing, code quality, code completion, and so on. These tools include Amazon CodeGuru for code analysis, and Amazon CodeWhisper for getting coding recommendations powered by machine learning algorithms.

In this edition of Let’s Architect!, we’ll show you some tools that every developer should consider including in their toolkit.

Cell-based architecture

Journey to Cloud-Native Architecture Series #7:  Using Containers and Cell-based design for higher resiliency and efficiency

In our previous Journey to Cloud-Native blogposts, we talked about evolving our architecture to become more scalable, secure, and cost effective to handle hyperscale requirements. In this post, we take these next steps: 1/ containerizing our applications to improve resource efficiency, and, 2/ using cell-based design to improve resiliency and time to production. Containerize applications […]

Let's Architect

Let’s Architect! Designing systems for stream data processing

Harnessing the potential of streaming data processing offers the opportunity to stay at the forefront of industries, make data-informed decisions with agility, and gain invaluable insights into customer behavior and operational efficiency.

Let's Architect

Let’s Architect! Designing systems for batch data processing

With this edition of Let’s Architect!, we’ll cover important things to keep in mind while working in the area of data engineering. Most of these concepts come directly from the principles of system design and software engineering. We’ll show you how to extend beyond the basics to ensure you can handle datasets of any size — including for training AI models.