$ show igorbarinov/awesome-data-engineering
This repository is a curated list (awesome list) of tools, frameworks, and resources for data engineering. It is not a software application or a data research project; rather, it serves as a community-maintained directory of databases, ingestion tools, stream processors, workflow orchestrators, data quality frameworks, and more. The list is organized into categories such as Databases, Data Ingestion, Stream Processing, Batch Processing, Workflow, Data Lake Management, Testing, and Community resources. Each entry includes a link and a brief description. The repository also includes contribution guidelines and CI workflows to maintain list quality. It is widely referenced (over 9,000 stars) and actively updated with new entries via pull requests. The primary value is as a reference for data engineers evaluating tools, not as executable code.
The LLM identified 2 files worth highlighting for reuse:
Defines inclusion criteria and contribution guidelines for a curated list. This file is self-contained and can be extracted and adapted for any awesome list or resource directory. It demonstrates non-obvious skill in setting clear, objective standards for submissions (e.g., requiring evidence of use, minimum age, licensed repositories) and provides a reusable template for community governance.
CI workflow that runs awesome-lint on pull requests to enforce list formatting and quality. This is a reusable pattern for any awesome list repository, ensuring consistency and reducing manual review. It is isolated, easy to copy, and demonstrates a practical application of automated linting for documentation-focused projects.