awesome-scalability open source analysis

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

Project overview

⭐ 67577 · Last activity on GitHub: 2026-01-04

GitHub: https://github.com/binhnguyennus/awesome-scalability

Why it matters for engineering teams

awesome-scalability addresses the practical challenges of designing systems that can handle growth in users, data, and transactions without sacrificing performance or reliability. It is particularly valuable for machine learning and AI engineering teams who need to build scalable backend architectures capable of supporting complex data processing and model deployment. The repository compiles proven patterns and best practices suitable for production ready solutions, reflecting mature approaches used in real-world large-scale systems. However, it is not the right choice for teams seeking quick, out-of-the-box tools or those working on small-scale projects where such complexity is unnecessary. The trade off lies in the learning curve and effort required to implement these scalable designs effectively.

When to use this project

This open source tool for engineering teams is ideal when planning or improving systems expected to grow significantly in scale or complexity. Teams should consider alternatives if their focus is on rapid prototyping or simple applications that do not demand extensive scalability or distributed system design.

Team fit and typical use cases

Machine learning and AI engineers gain the most from this repository, using it to guide the design of scalable data pipelines, distributed training environments, and reliable model serving infrastructure. It is commonly employed in products involving big data analytics, real-time processing, and web services that require robust backend architectures. Tech leads and system architects also use it as a reference for system design discussions and interview preparation.

Best suited for

Topics and ecosystem

architecture awesome awesome-list backend big-data computer-science design-patterns devops distributed-systems interview interview-practice interview-questions lists machine-learning programming resources scalability system system-design web-development

Activity and freshness

Latest commit on GitHub: 2026-01-04. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.