44 Best Resources to learn Data Engineering (YouTube, Books, Courses, & Tutorials)

44 Best Resources to learn Data Engineering (YouTube, Books, Courses, & Tutorials)

Aqsa Z. Aqsa Z.

Aqsa Z.

Founder @ MLTUT | Top Data Analytics Voice| 29K+ LinkedIn Community!

发布日期: 2024年5月5日
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Are you looking for the Best Resources to learn Data Engineering?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Data Engineering including Online Courses, Tutorials, Books, and YouTube Videos.

Now, without any further ado, let’s get started-

Best Resources to Learn Data Engineering

For your convenience, I have created separate tables for each resource. So let’s start with online courses-

Best Data Engineering Courses and Certifications

  1. Become a Data Engineer– Udacity
  2. Data Engineering, Big Data, and Machine Learning on GCP Specialization– Coursera
  3. Data Engineer with Python– Datacamp
  4. Big Data Specialization– Coursera
  5. Data Engineering with Google Cloud Professional Certificate– Coursera
  6. Data Warehousing for Business Intelligence Specialization– Coursera
  7. Modern Big Data Analysis with SQL Specialization– Coursera
  8. From Data to Insights with Google Cloud Platform Specialization– Coursera
  9. Data Engineering Basics for Everyone– edX
  10. Big Data and Hadoop Essentials– Udemy
  11. Python for Data Engineering Project- edX
  12. Data Wrangling with MongoDB– Udacity FREE Cours
  13. Intro to Hadoop and MapReduce– Udacity FREE Course
  14. Spark– Udacity FREE Course
  15. Introduction to Big Data– Coursera FREE Course

Best Books to Learn Data Engineering

  1. Data Engineering with Python by Paul Crickard
  2. Designing Data-Intensive Applications by Martin Kleppmann
  3. Spark: The Definitive Guide: Big Data Processing Made Simple by Bill Chambers, Matei Zaharia
  4. Data Science For Dummies by Lillian Pierson, Jake Porway
  5. The Data Warehouse Toolkit by Ralph Kimball, Margy Ross
  6. Building a Data Warehouse: With Examples in SQL Server by Vincent Rainardi
  7. Big Data: Principles and best practices of scalable realtime data systems by Nathan Marz, James Warren
  8. Fundamentals of Data Engineering: Plan and Build Robust Data Systems by Joe Reis, Matt Housley
  9. Data Engineering with AWS by Gareth Eagar
  10. Modern Data Engineering with Apache Spark by Scott Haines
  11. Azure Data Engineering Cookbook by Nagaraj Venkatesan, Ahmad Osama
  12. Data Engineering with Google Cloud Platform by Adi Wijaya

Best Data Engineering Tutorials

  1. What is Data Engineering?-> Intellipaat
  2. DATA ENGINEERING-> freecodecamp
  3. Data Warehouse Tutorial-> javaTpoint
  4. Data Engineer-> Dataquest
  5. Big Data & Analytics Tutorials-> TutorialsPoint
  6. Big Data Engineer-> Educba
  7. Azure for the Data Engineer-> Microsoft

领英推荐

Best Data Engineering YouTube Channels

  1. Big Data Engineer Full Course-> Simplilearn
  2. Data Engineering Course-> Intellipaat
  3. Data Engineering Full Hands-on Course-> The AI University
  4. Big Data & Hadoop Full Course-> Edureka
  5. ETL Tutorial for Beginners-> Edureka
  6. Data Engineering Project-> Darshil Parmar’s
  7. Python For Data Engineering-> TechLake
  8. Data Engineering Full Tutorial for Beginners-> Scaler
  9. AWS DATA ENGINEERING-> Learn by doing it
  10. Data Engineering Essentials using Spark, Python, and SQL-> itversity

And here the list ends. I hope these resources will help you to learn and master Data Engineering. I would suggest you bookmark this article for future referrals.

Now it’s time to wrap up.

Conclusion

In this article, I tried to cover the 44 Best Resources to Learn Data Engineering from online courses to YouTube videos. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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Richard Schiller

Chief Architect, CIO, Author, Enterprise Systems Architect

1 个月

Since this topic is core to a recent book that I wrote with David Larochelle; I'll share that with everyone: https://www.dhirubhai.net/posts/richardschiller_data-engineering-best-practices-architect-activity-7237806759854108672-wMuI? The book provides some specific guidance to questions that require answers are you develop robust and future-proof data processing systems.?The progression from data's raw state (DevOps) to information (DataOps), then knowledge (MLOps) and eventually insight (AIOps) is clarified.?Lastly, the preparation of enterprise and domain knowledge for generative AI use case tuning is addressed.?Take a look at the books outline and see if the topics resonate with your businesses.

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Creds

6 个月

Great job Aqsa Z.! Love to see you inspiring others with great resource:) Keep going, keep inspiring, we are cheering you on! ??????

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Kim Pramanik

6 个月

Nice and well researched. More importantly it is focused on one topic which is what matters.

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