An interactive map that categorizes and filters hundreds of cloud native projects and products with detailed metrics.
The CNCF Cloud Native Landscape is an interactive visualization tool that maps and categorizes hundreds of cloud native projects and products. It helps users navigate the complex ecosystem by filtering technologies and displaying detailed metrics like GitHub stars, funding, and contributor counts. The landscape serves as a reference guide for understanding available options when deploying cloud native applications.
Cloud architects, DevOps engineers, platform teams, and technology decision-makers who need to evaluate and select cloud native technologies for their infrastructure and applications.
It provides a centralized, up-to-date view of the entire cloud native ecosystem with verified data from GitHub and Crunchbase, saving users from manually researching hundreds of scattered projects. The interactive filtering and categorization make it uniquely valuable for discovery and comparison.
🌄 The Cloud Native Interactive Landscape filters and sorts hundreds of projects and products, and shows details including GitHub stars, funding, first and last commits, contributor counts and headquarters location.
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Enables dynamic filtering and sorting of hundreds of cloud native projects by criteria like category and GitHub stars, making discovery efficient. This is highlighted in the README's Interactive Filtering feature.
Displays detailed data including GitHub stars, funding, commit history, and contributor counts sourced from GitHub and Crunchbase. The README notes this provides a data-rich interface for evaluation.
New entries are added via pull requests to the landscape.yml file with clear guidelines, ensuring the landscape reflects community contributions. The README emphasizes this collaborative approach.
Regenerates daily with the latest information from sources like GitHub and Crunchbase, keeping the landscape current. The README confirms updates are visible within 24 hours of merging changes.
The README states that new categories are unlikely to be created, which can make it difficult for emerging or niche technologies to find a proper fit, limiting adaptability.
Errors in metrics from Crunchbase or GitHub must be fixed at the source, not directly in the landscape, leading to potential delays and inaccuracies in displayed data.
Changes via pull requests take up to 24 hours to appear due to daily regeneration, making it unsuitable for scenarios needing instant data reflection.