FactGraph VS data_engineering_on_gcp_book

I used to have a project like this. I was going to call it FactGraph: https://github.com/FactGraph/FactGraph/wiki

My idea was to build up a big community-maintained database containing facts and evidence, where everything is linked into a huge network. Everything would have a weight (sometimes automatically calculated from parent nodes), and the software would calculate probabilities for some big questions. Every user could also build their own personalized graph to explore their own worldview, and maybe even uncover some cognitive dissonance that they weren't aware of. Or you could use it to compare and contrast different philosophies, religions. Could even calculate a "coherence score" for each religion and denomination after crunching all of the available evidence.

Then I discovered RootClaim: https://www.rootclaim.com

They're doing something very similar, with a more targeted approach where they focus on some specific questions. e.g. COVID-19: https://www.rootclaim.com/analysis/what-is-the-source-of-cov...

RootClaim really seems to be nailing it so far, and hopefully they can continue to grow and become something like the project I was imagining.

This book will walk you through setting up a complete data engineering stack on GCP: https://github.com/Nunie123/data_engineering_on_gcp_book

As for resources, this book goes over a lot of these: https://github.com/Nunie123/data_engineering_on_gcp_book. However, this goes over the 'how', not the 'why'. The only method I know for understanding the 'why' is experience. Whether at work or personal projects.

Here's a good book to follow along to introduce you to common tooling and design patterns: https://github.com/Nunie123/data_engineering_on_gcp_book

I'm not sure if this is exactly what you're looking for, but here's a book on GitHub that talks about the tools and steps for building data pipelines into a data warehouse: https://github.com/Nunie123/data_engineering_on_gcp_book

Check out this book: https://github.com/Nunie123/data_engineering_on_gcp_book

If you're interested in example of how to build a complete data engineering infrastructure, you should check out this book: https://github.com/Nunie123/data_engineering_on_gcp_book

If you want to try Airflow without the setup headache, you can try Composer on GCP, which is a hosted version of Airflow. I wrote some info on how to do that here: https://github.com/Nunie123/data_engineering_on_gcp_book/blob/master/ch_2_orchestration.md

This book might be a good resource for you: https://github.com/Nunie123/data_engineering_on_gcp_book

If you want a place to start on personal projects you can check out this book, https://github.com/Nunie123/data_engineering_on_gcp_book, which will walk you through the basics of setting up a full data engineering stack.

If you are interested in tooling, here's a free book on setting up a basic data engineering tech stack on GCP: https://github.com/Nunie123/data_engineering_on_gcp_book