# DataExpert-io/data-engineer-handbook **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/dataexpert-io-data-engineer-handbook).** 40,217 stars · 7,658 forks · Jupyter Notebook ## Links - GitHub: https://github.com/DataExpert-io/data-engineer-handbook - awesome-repositories: https://awesome-repositories.com/repository/dataexpert-io-data-engineer-handbook.md ## Topics `apachespark` `awesome` `bigdata` `data` `dataengineering` `sql` ## Description This project is a comprehensive, community-driven knowledge base designed to support individuals pursuing careers in data engineering. It functions as a centralized learning hub that aggregates industry best practices, technical documentation, and educational resources to assist with both professional development and the design of robust data pipeline architectures. The repository distinguishes itself by providing a structured technical career roadmap that includes curated learning paths, interview preparation strategies, and practical project examples. By indexing a diverse range of media—including blogs, podcasts, books, and whitepapers—it offers a unified directory for staying current with industry trends and mastering the specific skills required for data engineering roles. The content is organized as a collection of structured markdown files, which facilitates community contributions and version control through standard git workflows. This documentation is rendered into a searchable web interface, providing an accessible and navigable resource for practitioners at all levels of experience. ## Tags ### Education & Learning Resources - [Data Engineering Curricula](https://awesome-repositories.com/f/education-learning-resources/data-engineering-curricula.md) — A comprehensive collection of educational materials, industry best practices, and professional development guides for individuals pursuing careers in data engineering. - [Career Development Paths](https://awesome-repositories.com/f/education-learning-resources/career-development-paths.md) — Navigating the professional landscape of data engineering by accessing curated learning paths, certifications, and industry-standard educational resources. - [Project Ideas](https://awesome-repositories.com/f/education-learning-resources/project-ideas.md) — - End-to-end Uber Data engineering project with BigQuery - Build a pipeline with LLMs - Lecture - Lab - Build a SQL query engine with LLMs and LangChain - Lecture - Lab - Extract Metadata from Youtube Videos in AWS with ([source](https://github.com/DataExpert-io/data-engineer-handbook/blob/main/projects.md)) - [Technical Career Roadmaps](https://awesome-repositories.com/f/education-learning-resources/technical-career-roadmaps.md) — A structured guide offering advice on interview preparation, skill acquisition, and networking strategies for aspiring and practicing data professionals. - [Technical Interview Guides](https://awesome-repositories.com/f/education-learning-resources/technical-interview-guides.md) — Mastering the specific technical skills and problem-solving patterns required to successfully pass data engineering job interviews at top companies. - [Industry Knowledge Sources](https://awesome-repositories.com/f/education-learning-resources/industry-knowledge-sources.md) — Staying current with the latest industry trends, tools, and best practices through a comprehensive collection of newsletters, blogs, and podcasts. ### Miscellaneous Curated Lists - [Curated Lists](https://awesome-repositories.com/f/miscellaneous-curated-lists/curated-lists.md) — No releases published ([source](https://github.com/DataExpert-io/data-engineer-handbook#readme)) - [Knowledge Repositories](https://awesome-repositories.com/f/miscellaneous-curated-lists/knowledge-repositories.md) — Relies on external contributions and pull requests to maintain an up-to-date repository of industry best practices and educational materials. - [Data Pipeline Knowledge Bases](https://awesome-repositories.com/f/miscellaneous-curated-lists/data-pipeline-knowledge-bases.md) — A community-driven repository of technical documentation, design patterns, and curated links to external resources for building data pipelines. - [Resource Directories](https://awesome-repositories.com/f/miscellaneous-curated-lists/resource-directories.md) — Categorizes diverse media types including blogs, podcasts, and code repositories into a unified directory for streamlined discovery by data engineers. - [Data Engineering Curated Lists](https://awesome-repositories.com/f/miscellaneous-curated-lists/data-engineering-curated-lists.md) — Data Cleaning Best Practices — a named example documented in this learning resource. ([source](https://github.com/DataExpert-io/data-engineer-handbook/blob/main/data_cleaning.md)) ### Software Engineering & Architecture - [Data Architecture Patterns](https://awesome-repositories.com/f/software-engineering-architecture/data-architecture-patterns.md) — Learning to design and implement robust data processing systems by studying established design patterns and real-world project examples. ([source](https://github.com/DataExpert-io/data-engineer-handbook#readme)) ### Content Management & Publishing - [Static Documentation Generators](https://awesome-repositories.com/f/content-management-publishing/static-documentation-generators.md) — Generates a searchable and navigable web interface from a collection of structured markdown files to provide a centralized learning hub. - [Markdown Documentation Systems](https://awesome-repositories.com/f/content-management-publishing/markdown-documentation-systems.md) — Organizes technical knowledge and learning resources into a structured hierarchy of plain text files for easy versioning and community contribution. ### Programming Languages & Runtimes - [Programming Languages](https://awesome-repositories.com/f/programming-languages-runtimes/programming-languages.md) — - Jupyter Notebook 66.9% - Python 26.3% - Makefile 3.7% - Dockerfile 2.0% - Shell 1.1% ([source](https://github.com/DataExpert-io/data-engineer-handbook#readme))