listudy VS data_engineering_on_gcp_book

It comes with experience. There is a website called listudy.org that has a section called “Blind Tactics” that might help with visualization.

Listudy: Memorise openings with spaced repetition. Chess Endgame Training Chessercise: Practice chess with YouTube. Chess Madra: Build and practise an opening repertoire. Aimchess: Learn your strengths and weaknesses.

If you are trying to practice a certain opening, I would highly recommend listudy.org just to build that spaced repetition in your head.

I find https://listudy.org very useful for drilling lines. I don't see it mentioned often, but it allows you to import any lichess study (selfmade or other) into it and it'll pick a random line that's up to you to correctly play out until the end. Works very well if you do it enough

I use private Lichess Studies to store/build my repertoire. Start "choosing" your preferred responses to various opening moves and store them there. Start 1 move deep at a time and branch out slowly. At low level of play it's actually better to go just a few moves deep and have some canned responses to common bad moves from your opponents. You can import your lichess study into listudy.org which turns your saved opening prep into spaced repetition exercises to practice.

listudy.org

Second, you can check out https://listudy.org which can do this with lichess studies.

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