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Data Engineering Learning Roadmap

Ranking updated Jun 30, 2026

For a step-by-step path to becoming a data engineer, the strongest matches are andkret/cookbook (This repository is a curated knowledge base and career), datatalksclub/data-engineering-zoomcamp (The Data Engineering Zoomcamp is a structured, self-paced curriculum) and amai-gmbh/ai-expert-roadmap (This repository is a structured learning roadmap for artificial). mage-ai/mage-ai and pkuflyingpig/cs-self-learning round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.

Curated resources, tutorials, and technical guides for mastering essential data engineering skills and infrastructure tools.

Data Engineering Learning Roadmap

Find the best repos with AI.We'll search the best matching repositories with AI.
  • andkret/cookbookandkret avatar

    andkret/Cookbook

    15,161View on GitHub↗

    Cookbook is a comprehensive knowledge base and reference repository for data engineering. It serves as a centralized directory for data architecture patterns, professional career roadmaps, and a curated collection of public datasets. The project provides a structured guide for transitioning into specialized data engineering roles through skill-matrix mapping and technical interview preparation. It further distinguishes itself by documenting real-world industry case studies and decomposing large-scale industrial implementations into repeatable architectural patterns. The repository covers a b

    This repository is a curated knowledge base and career roadmap for data engineering, offering structured guides, skill-matrix mapping, and architectural patterns that cover key areas like big data and data warehousing, making it a strong fit for a comprehensive learning plan.

    PythonBig Data Architectures
    View on GitHub↗15,161
  • datatalksclub/data-engineering-zoomcampDataTalksClub avatar

    DataTalksClub/data-engineering-zoomcamp

    42,483View on GitHub↗

    This project is an open-source educational curriculum designed to provide comprehensive training in data engineering. It focuses on building scalable data pipelines and managing cloud-native infrastructure through a structured, self-paced program that combines technical explanations with hands-on practical exercises. The curriculum distinguishes itself by emphasizing industry-standard methodologies, specifically teaching students how to implement infrastructure as code and manage data workflows through orchestration tools. By utilizing container-based environment isolation and declarative con

    The Data Engineering Zoomcamp is a structured, self-paced curriculum that covers ETL pipelines, big data tools (Spark, Kafka), containerization, cloud infrastructure, and data warehousing with hands-on projects—exactly the comprehensive learning roadmap for data engineering you are looking for.

    Jupyter NotebookData Engineering CurriculaData EngineeringCloud Infrastructure Courses
    View on GitHub↗42,483
  • amai-gmbh/ai-expert-roadmapAMAI-GmbH avatar

    AMAI-GmbH/AI-Expert-Roadmap

    31,091View on GitHub↗

    This project is a professional development repository that provides structured learning paths for individuals pursuing careers in data-centric engineering and artificial intelligence. It functions as a competency benchmarking framework, defining the core knowledge areas and technical milestones required to achieve proficiency in specialized domains. The repository distinguishes itself through hierarchical knowledge graphing, which organizes complex technical subjects into nested tree structures to create clear, progressive learning sequences. By centralizing curated educational resources and

    This repository is a structured learning roadmap for artificial intelligence and data science, not a dedicated data engineering path—while it touches on data-related topics, it lacks the explicit focus on ETL pipelines, data warehousing, and big data technologies that a data engineering roadmap requires.

    JavaScriptLearning PathsLearning Roadmaps
    View on GitHub↗31,091
  • mage-ai/mage-aimage-ai avatar

    mage-ai/mage-ai

    8,759View on GitHub↗

    Mage AI is a Python-based data pipeline orchestrator and self-hosted data integrated development environment. It is designed for building, scheduling, and monitoring data workflows using a block-based pipeline design and interactive notebook interface. The platform distinguishes itself by integrating generative AI capabilities, allowing users to connect large language model providers via API to incorporate artificial intelligence into automated data streams. It also functions as an Apache Spark data processor, managing the kernels and infrastructure required for high-volume analytics and larg

    Mage AI is a data pipeline orchestration tool, not a structured learning roadmap for data engineering; it supports ETL and big data but does not provide a guided progression or curriculum.

    PythonETL WorkflowsApache Spark Pipelines
    View on GitHub↗8,759
  • pkuflyingpig/cs-self-learningPKUFlyingPig avatar

    PKUFlyingPig/cs-self-learning

    73,191View on GitHub↗

    This project is a centralized repository and academic resource aggregator designed to guide students through a structured computer science curriculum. It provides a comprehensive roadmap of foundational courses and technical materials, helping learners navigate the transition from introductory programming to advanced software engineering proficiency. The repository distinguishes itself through a community-driven approach, where study paths and resource collections are refined and expanded via peer feedback and collaborative contributions. By organizing high-quality lecture notes, assignments,

    This repository offers a structured curriculum roadmap for general computer science, but it does not specifically target data engineering topics like ETL, big data, data modeling, or cloud platforms, so it is not the specialized learning plan you are looking for.

    HTMLLearning Paths
    View on GitHub↗73,191
  • moabukar/tech-vaultmoabukar avatar

    moabukar/tech-vault

    3,351View on GitHub↗

    tech-vault is a command-line technical interview bank and knowledge base designed for practicing engineering questions across various technical domains. It functions as a terminal-based application that stores structured study materials and interview questions as markdown files, which are then rendered directly within the system console. The project distinguishes itself through a delivery model that uses command-line argument parsing to filter content by topic or difficulty. It also includes a random selection algorithm to pick individual questions from the collection for spontaneous study se

    This repository is a command-line interview bank covering many engineering topics, including data warehousing and data modeling, but it is a practice and review tool rather than a structured learning roadmap with a clear progression path for becoming a data engineer from scratch.

    HCLData WarehousingData Modeling and Schemas
    View on GitHub↗3,351
  • bregman-arie/devops-exercisesbregman-arie avatar

    bregman-arie/devops-exercises

    82,879View on GitHub↗

    This project is a comprehensive educational curriculum designed to build proficiency across modern infrastructure, cloud-native technologies, and systems administration. It functions as a reference library and interview preparation resource, offering a structured collection of conceptual questions, practical coding challenges, and hands-on scenarios that cover the full spectrum of software delivery and operational workflows. The repository distinguishes itself through a modular, domain-specific structure that links instructional problem statements with verified implementation examples. By emp

    This repository is a structured DevOps and infrastructure learning curriculum, not a data engineering roadmap—it covers cloud platforms and SQL as DevOps context, but lacks the core data engineering topics like ETL pipelines, big data technologies, data modeling, and data warehousing you need.

    PythonData Ingestion PipelinesLearning Paths
    View on GitHub↗82,879
  • vonng/ddiaVonng avatar

    Vonng/ddia

    22,648View on GitHub↗

    This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

    This repository is a companion to the book "Designing Data-Intensive Applications," covering distributed systems and databases, but it is a technical reference rather than a structured, step-by-step learning roadmap with hands-on projects and a clear progression path for becoming a data engineer.

    PythonData WarehousingETL Workflows
    View on GitHub↗22,648
  • kamranahmedse/developer-roadmapkamranahmedse avatar

    kamranahmedse/developer-roadmap

    357,434View on GitHub↗

    Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth. The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps

    This is a general software engineering roadmap platform covering roles like backend, frontend, and devops, but it does not include a dedicated data engineering track, so it only partially addresses the search for a structured data engineering learning path.

    TypeScriptLearning Paths
    View on GitHub↗357,434
  • microsoft/data-science-for-beginnersmicrosoft avatar

    microsoft/Data-Science-For-Beginners

    35,657View on GitHub↗

    This project is a comprehensive educational curriculum designed to teach the fundamental concepts, workflows, and tools of data science. It provides a structured learning path that covers the end-to-end data science lifecycle, including data acquisition, maintenance, processing, and pattern discovery, while grounding theoretical knowledge in practical, real-world applications. The curriculum distinguishes itself through a data-driven pedagogical design that utilizes interactive, notebook-based lessons. By combining narrative text with live code blocks, the platform allows learners to experime

    This is a structured curriculum for learning data science, not data engineering—it covers the data science lifecycle and analytics tools, but lacks the focus on ETL pipelines, big data technologies, and cloud platforms that a data engineering roadmap requires.

    Jupyter NotebookData Science CurriculaData Visualization TutorialsInteractive Notebooks
    View on GitHub↗35,657
  • delta-io/deltadelta-io avatar

    delta-io/delta

    8,596View on GitHub↗

    Delta is a lakehouse table format that brings ACID transactions and data warehouse consistency to large scale data lakes on cloud object storage. It serves as an ACID transaction manager, coordinating atomic commits and serializable isolation for concurrent reads and writes across distributed compute engines. The project provides a multi-engine interoperability layer that uses format translation to allow diverse SQL engines and processing frameworks to read and write the same tables. It functions as a data versioning system, utilizing a transaction log to enable time travel, historical snapsh

    Delta is a lakehouse table format and ACID transaction manager for data lakes, not a curated learning roadmap or plan for becoming a data engineer, so it does not match the request for a structured progression path.

    ScalaData Warehousing Patterns
    View on GitHub↗8,596

Related searches

  • a structured path into ML engineering
  • a learning path for DevOps engineers
Compare the top 10 at a glance
RepositoryStarsLanguageLicenseLast push
andkret/cookbook15.2KPythonApache-2.0Jun 12, 2026
datatalksclub/data-engineering-zoomcamp42.5KJupyter Notebook—Jun 10, 2026
amai-gmbh/ai-expert-roadmap31.1KJavaScriptMITSep 12, 2025
mage-ai/mage-ai8.8KPythonApache-2.0Jun 24, 2026
pkuflyingpig/cs-self-learning73.2KHTMLMITFeb 24, 2026
moabukar/tech-vault3.4KHCLapache-2.0Feb 17, 2026
bregman-arie/devops-exercises82.9KPythonNOASSERTIONDec 27, 2025
vonng/ddia22.6KPythoncc-by-4.0Feb 15, 2026
kamranahmedse/developer-roadmap357.4KTypeScriptNOASSERTIONJun 16, 2026
microsoft/data-science-for-beginners35.7KJupyter NotebookMITJun 10, 2026
a path to becoming a cloud engineer
  • a learning path for SRE roles
  • a learning path into security engineering
  • a structured learning path for software developers
  • a structured path to becoming a backend engineer
  • a structured curriculum for self-taught computer science