@n7wb1l: The Ultimate Panorama and Roadmap for Data Engineering! A Free, One-Stop Learning Path Library for Data Engineers: The Data Engineering Handbook With the rapid evolution of big data, real-time stream processing, and LLM/AI data infrastructure, Data Engineering has become one of the hottest and most demanding fields in tech. The Data Engineering Handbook is a free, one-stop learning roadmap library covering hands-on projects, interview guides, classic book recommendations, communities, and email newsletters.

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Summary

An introduction to The Data Engineering Handbook (~45k Stars on GitHub), an open-source project led by former Meta/Airbnb data engineer Zach Wilson. It is a one-stop learning roadmap library for data engineering, covering hands-on projects, interview prep, curated book lists, communities, email newsletters, and more.

The Ultimate Panorama and Roadmap for Data Engineering! A Free, One-Stop Learning Path Library for Data Engineers: The Data Engineering Handbook 📊🚀 With the rapid evolution of big data, real-time stream processing, and LLM/AI data infrastructure, Data Engineering has become one of the hottest and most demanding fields in the tech world. Yet facing the dizzying array of ETL tools, data warehouses, stream processing frameworks, and distributed architectures, many beginners and career switchers often find themselves stuck — unable to find systematic learning materials or hands-on projects. The Data Engineering Handbook (DataExpert-io/data-engineer-handbook) is led by Zach Wilson, a senior data engineering expert formerly at Meta and Airbnb. It has racked up approximately 45k Stars on GitHub (44,640 Stars, with nearly 10k forks)! Hailed as the "ultimate resource collection" for data engineering, it lays out everything you need — from zero-basics to becoming a top-tier data engineer — with high-quality learning paths and practical resources. 👥 【Who Is This For】 Data engineering beginners and career switchers: Learners who want to build a systematic data engineering knowledge base and are looking for a complete roadmap from basic syntax to distributed architecture. Data engineering job seekers and interview preppers: Candidates preparing for DE interviews at top tech giants or high-growth startups who need structured practice questions, architecture design, and interview coaching. Data analysts / software engineers: Tech professionals looking to transition or level up into Data Engineering or Data Infrastructure roles. 🎯 【5 Killer Core Sections & Resource Highlights】 1️⃣ Hands-on Projects: No armchair theory! Rich real-world project examples covering end-to-end ETL data pipeline building, Data Warehouse modeling, Streaming, and other hardcore practices. 2️⃣ Data Engineering Interview Prep: A collection of real DE interview experience and tips from top tech companies, covering advanced SQL, distributed system design, data modeling, and data algorithms — the core interview dimensions. 3️⃣ Curated Must-Read Books: A carefully selected list of authoritative data engineering books (such as *Designing Data-Intensive Applications*, *Data Warehouse Toolkit*, and more) to help you build a solid theoretical foundation. 4️⃣ Thriving Communities: Hand-picked active data engineering communities and Slack/Discord groups from around the world, making it easy to network with peers, get questions answered, and stay on top of industry trends. 5️⃣ Newsletters & Updates: A roundup of top-tier data engineering newsletters, delivering hardcore knowledge on data infrastructure, Lakehouse architecture, and AI data pipelines straight to your inbox. 💡 【Tech Stack & Repository Composition】 Core content breakdown: Includes a large number of interactive Jupyter Notebooks (66.9%) and hands-on Python code (26.3%), jointly maintained by 130+ open-source contributors. ⭐ Stars: ~45k (44,640 Stars) 🔗 Open-source repository: https://github.com/DataExpert-io/data-engineer-handbook #DataEngineering #数据工程 #BigData #Python #ETL #DataWarehouse #面试指南 #开源项目 #开发者工具

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The Ultimate Guide to Data Engineering: A Complete, Free, One-Stop Learning Roadmap for Aspiring Data Engineers — The Data Engineering Handbook 📊🚀

With the rapid evolution of big data, real-time stream processing, and LLM/AI data infrastructure, data engineering has become one of the hottest and most challenging fields in tech. However, faced with the overwhelming number of ETL tools, data warehouses, stream processing frameworks, and distributed architectures, many beginners and career-changers often struggle with “finding structured learning materials” or “lacking hands-on projects.”

The Data Engineering Handbook (DataExpert-io/data-engineer-handbook), led by Zach Wilson — a senior data engineering expert formerly at Meta/Airbnb — has amassed approximately 45k Stars on GitHub (44,640 Stars, with nearly 10,000 Forks)! Dubbed the “ultimate resource collection” for data engineering, it lays out all the quality learning paths and practical resources you need to go from complete beginner to top-tier data engineer.

👥 Who Is This For?

Data engineering beginners and career-changers: Those looking to build a systematic data engineering knowledge base and seeking a complete roadmap — from basic syntax to distributed architecture
Data engineers preparing for job interviews: Candidates preparing for data engineering (DE) interviews at tech giants or high-growth startups who urgently need structured practice questions, architecture design guidance, and interview techniques
Data analysts / software engineers: Professionals looking to cross over into data engineering or data infrastructure

🎯 5 Killer Core Sections & Resource Highlights

1️⃣ Hands-on Projects: Say goodbye to theory-only learning! Rich, real-world project examples covering end-to-end ETL pipeline construction, data warehouse modeling, real-time stream processing, and more.

2️⃣ Interview Prep: Practical DE interview experience and tips from top tech companies, covering SQL advanced techniques, distributed system design, data modeling, and data algorithms — all core interview dimensions.

3️⃣ Curated Books: A carefully selected list of authoritative data engineering books (such as Designing Data-Intensive Applications, Data Warehouse Toolkit, etc.) to help you build a solid theoretical foundation.

4️⃣ Thriving Communities: Handpicked, the most active data engineering communities and Slack/Discord groups worldwide — perfect for exchanging ideas, getting answers, and staying on top of industry trends.

5️⃣ Newsletters & Updates: A roundup of top-tier data engineering newsletters, delivering in-depth knowledge on data infrastructure, lakehouse architecture, and AI data pipelines straight to your inbox.

💡 Tech Languages & Repository Composition

Core content breakdown: Comprised mainly of interactive Jupyter Notebooks (66.9%) and Python code (26.3%), maintained collaboratively by 130+ open-source contributors.

⭐ Stars: ~45k (44,640 Stars) 🔗 Open-source repo: https://github.com/DataExpert-io/data-engineer-handbook

DataExpert-io/data-engineer-handbook

Source: https://github.com/DataExpert-io/data-engineer-handbook

The Data Engineering Handbook

This repo has all the resources you need to become an amazing data engineer!

Join the free Databricks AI boot camp on August 3rd here (https://learn.dataexpert.io/program/the-one-week-beginners-databricks-boot-camp-7129) and signup for Databricks Free Edition here (https://signup.databricks.com/?provider=DB_FREE_TIERutm_source=github&utm_medium=video&utm_campaign=DataExpert)

Getting started

If you are new to data engineering, start by following this 2024 breaking into data engineering roadmap

If you are here for the 4-week free beginner boot camp you can check out:

If you are here for the 6-week free intermediate boot camp you can check out

For more applied learning:

Check out the projects section for more hands-on examples!
Check out the interviews section for more advice on how to pass data engineering interviews!
Check out the books section for a list of high quality data engineering books
Check out the communities section for a list of high quality data engineering communities to join
Check out the newsletter section to learn via email

Resources

Great list of over 25 books

Great list of over 10 communities to join:

Top must-join communities for DE:

Top must-join communities for ML:

Companies:

Orchestration
- Mage
- Astronomer
- Prefect
- Dagster
- Airflow
- Kestra
- Shipyard
- Hamilton
Data Lake / Cloud
- Tabular
- Microsoft
- Databricks
- Onehouse
- Delta Lake
- Ilum
- DuckLake
- Apache Iceberg
- Apache Polaris
- Lakekeeper
Data Warehouse
Data Quality
- dbt
- Metaplane
- Gable
- Great Expectations
- Streamdal
- Coalesce
- Soda
- DQOps
- HEDDA.IO
- Dingo
Education Companies
Analytics / Visualization
- Preset
- Starburst
- Metabase
- Looker Studio
- Tableau
- Power BI
- Hex
- Apache Superset
- Evidence
- Redash
- Lightdash
Data Integration
- Cube
- Fivetran
- Airbyte
- dlt
- Sling
- Meltano
- Estuary
- Arpe.io
Semantic Layers
- Cube
- dbt Semantic Layer
Modern OLAP
- Apache Druid
- ClickHouse
- Apache Pinot
- Apache Kylin
- DuckDB
- QuestDB
- StarRocks
LLM application library
Real-Time Data
Data Lineage
- OpenLineage

Data Engineering blogs of companies:

Data Engineering Whitepapers:

Social Media Accounts

Here’s the most comprehensive list of data engineering creators:

(You have to have at least 5k followers somewhere to be added!)

YouTube

Name	YouTube Channel	Follower Count
ByteByteGo	ByteByteGo	1,000,000+
Data with Baraa	Data with Baraa	195,000+
Zach Wilson	Data with Zach	150,000+
Shashank Mishra	E-learning Bridge	100,000+
Seattle Data Guy	Seattle Data Guy	100,000+
TrendyTech	TrendyTech	100,000+
Darshil Parmar	Darshil Parmar	100,000+
Andreas Kretz	Andreas Kretz	100,000+
The Ravit Show	The Ravit Show	100,000+
Guy in a Cube	Guy in a Cube	100,000+
Adam Marczak	Adam Marczak	100,000+
nullQueries	nullQueries	100,000+
TECHTFQ by Thoufiq	TECHTFQ by Thoufiq	100,000+
SQLBI	SQLBI	100,000+
Alex Freberg	Alex The Analyst	100,000+
Ankur Ranjan	Big Data Show	100,000+
Prashanth Kumar Pandey	ScholarNest	77,000+
ITVersity	ITVersity	67,000+
Soumil Shah	Soumil Shah	50,000
Ansh Lamba	Ansh Lamba	18,000+
Azure Lib	Azure Lib	10,000+
Advancing Analytics	Advancing Analytics	10,000+
Kahan Data Solutions	Kahan Data Solutions	10,000+
Ankit Bansal	Ankit Bansal	10,000+
Mr. K Talks Tech	Mr. K Talks Tech	10,000+
Samuel Focht	Python Basics	10,000+
Mehdi Ouazza	Mehdio DataTV	3,000+
Alex Merced	Alex Merced Data	N/A
John Kutay	John Kutay	N/A
Emil Kaminski	Databricks For Professionals	5,000+

Name	LinkedIn Profile	Follower Count
Zach Wilson	Zach Wilson	400,000+
Chip Huyen	Chip Huyen	250,000+
Shashank Mishra	Shashank Mishra	100,000+
Seattle Data Guy	Ben Rogojan	100,000+
TrendyTech	Sumit Mittal	100,000+
Darshil Parmar	Darshil Parmar	100,000+
Andreas Kretz	Andreas Kretz	100,000+
ByteByteGo (Alex Xu)	Alex Xu	100,000+
Azure Lib (Deepak Goyal)	Deepak Goyal	100,000+
Alex Freberg	Alex Freberg	100,000+
SQLBI (Marco Russo)	Marco Russo	50,000+
Ankit Bansal	Ankit Bansal	50,000+
Marc Lamberti	Marc Lamberti	50,000+
Ankur Ranjan	Ankur Ranjan	48,000+
ITVersity (Durga Gadiraju)	Durga Gadiraju	48,000+
Prashanth Kumar Pandey	Prashanth Kumar Pandey	37,000+
Alex Merced	Alex Merced	30,000+
Ijaz Ali	Ijaz Ali	24,000+
Mehdi Ouazza	Mehdi Ouazza	20,000+
Ananth Packkildurai	Ananth Packkildurai	18,000+
Ansh Lamba	Ansh Lamba	13,000+
Manojkumar Vadivel	Manojkumar Vadivel	12,000+
Advancing Analytics	Simon Whiteley	10,000+
Li Yin	Li Yin	10,000+
Jaco van Gelder	Jaco van Gelder	10,000+
Joseph Machado	Joseph Machado	10,000+
Eric Roby	Eric Roby	10,000+
Simon Späti	Simon Späti	10,000+
Constantin Lungu	Constantin Lungu	10,000+
Lakshmi Sontenam	Lakshmi Sontenam	9,500+
Dani Pálma	Daniel Pálma	9,000+
Soumil Shah	Soumil Shah	8,000+
Arnaud Milleker	Arnaud Milleker	7,000+
Dimitri Visnadi	Dimitri Visnadi	7,000+
Lenny	Lenny A	6,000+
Dipankar Mazumdar	Dipankar Mazumdar	5,000+
Daniel Ciocirlan	Daniel Ciocirlan	5,000+
Hugo Lu	Hugo Lu	5,000+
Tobias Macey	Tobias Macey	5,000+
Marcos Ortiz	Marcos Ortiz	5,000+
Julien Hurault	Julien Hurault	5,000+
John Kutay	[John Kutay](https:/