Great O'Reilly Book Resources to gain expertise in Data Engineering

Summary: Danny Lee announced that their community has been accepted into the O'Reilly Community Partnership Program, offering free access to resources such as books and videos. In return, the community is encouraged to share book reviews and promote these resources on social media and in personal networks. A list of recommended books was shared, including titles related to data engineering and AI. Ahmed Mahmoud congratulated the community and emphasized the value of the book list as foundational references in data engineering and AI systems. They suggested organizing the reading material by experience level and incorporating interactive elements like discussion sessions and hands-on projects to enhance learning and application.

AI Summary

We have been accepted to the O'Reilly Community Partnership Program as of April 1st (not an April Fools Joke! 🙌). The program will give us access to free resources, books and videos. In return, we can share book reviews, our support in social media and in our personal networks and interactions. I really do hope our community will take advantage of this program and share the books and topics they are most interested in, and we can look at getting access to eBooks as well as physical copies to share in the group.

Below is a list of books that I think would be of interest to our community. If you see others on the O'Reilly website please add a comment or post in this Discussions group!

2025

2024

2022

Fundamentals of Data Engineering

2018

Spark: The Definitive Guide

2017

2015

Bioinformatics Data Skills

1 comment

Ahmed Mahmoud

4 weeks ago

Congratulations on joining the O’Reilly Community Partnership Program 👏🔥 This is a great milestone for the community and a strong step toward building real momentum in Data & AI learning and collaboration.

Honestly, the book list is incredibly valuable—these are not just books, they’re foundational references for anyone working in Data Engineering, Databricks, and AI systems. Titles like Designing Data-Intensive Applications and Fundamentals of Data Engineering are basically must-reads for anyone serious in the field.

I think we can make the most out of this opportunity by structuring it in a more community-driven way:

Create reading tracks based on experience levels (Beginner / Intermediate / Advanced)
Focus on one book each month and host a discussion or review session around it
Share practical takeaways and connect concepts directly to Databricks, Spark, and Delta Lake use cases
Turn key ideas into small hands-on projects so we don’t just read—but actually apply

Also, books like AI Engineering (2024) and Building Medallion Architectures (2025) would be perfect topics for upcoming meetups since they align directly with modern data platforms and real-world architecture patterns.

Really excited about how this can evolve into a highly active learning community where we don’t just consume knowledge—but build and experiment together

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