dali VS data_engineering_on_gcp_book

Tangentially related, I've written a barebones assembler for Android .apk files once (strictly speaking, the assembler is for .dex files, but it also comes with a set of tools to package and sign .apk files). It's written mainly in Nim and provides enough primitives to allow creating Java "stubs" for native .so libraries, so that .apk-s can be built in Nim WITHOUT JDK AT ALL. The Android NDK is still kinda needed/useful, though IIRC mainly for access to adb, and especially adb logcat (which you'll need A LOT for debugging if you try to use this contraption).

I'd love to One Day™ Rewrite It In Rust.

The .dex assembler itself is at: https://github.com/akavel/dali — you may like to check out the tests at: https://github.com/akavel/dali/tree/master/tests to see how using it looks like.

An example project with a simple .apk written purely in Nim (NO JDK) is at: https://github.com/akavel/hellomello/tree/flappy (unfortunately, given Nim's poor packaging story, it's most probably already bitrotten to the extent that it can't be quickly and easily built & used out of the box). I recorded a presentation about this for an online Nim conference — see: https://www.youtube.com/watch?v=wr9X5NCwPlI&list=PLxLdEZg8DR...

https://github.com/akavel/dali was one (a fully hand-written assembler for Android .apk files); I managed to write a rudimentary flappy-bird-like prototype in it and did a presentation: https://www.youtube.com/watch?v=wr9X5NCwPlI&list=PLxLdEZg8DR... but on shelf now, didn't get much attention, and I don't feel bad about it. Had some roadblocks, but managed to overcome them, and I'm honestly surprised how the core effort was basically easy to implement and how the formats were open and relatively simple. (The main real issues I had were that debugging via adb logs was tiresome when something was not working.) What was funny about this project was that I started it with basically a thought of: "there will be probably some annoying roadblock at some point that will make it unviable to continue; I accept that and will be ok with stopping once I stumble upon it; but I don't see one clearly from the start [I did some quick initial research how the formats & the bytecode look and they seemed rather simple], and I'm really curious how far I can get if I decide to not think about this possible roadblock". Turns out I was able to get all the way to the end :D

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