Yue VS data_engineering_on_gcp_book

For a recent project I chose Yue (https://libyue.com/), a cross-platform native widget GUI toolkit with C++, JavaScript/Node.js, and Lua. I've only used the Lua interface and macOS backend, but it has worked quite well, despite the very steep learning curve. This was also my first desktop GUI app, so I had to learn many implicit concepts that weren't obvious from the otherwise extensive documentation.

Yue was also the only option that 1) supported macOS, 2) supported Lua, 3) was sufficiently comprehensive to build a non-toy GUI app, 4) and that I could integrate into my (static) build. I couldn't even get the wxWidgets Lua interfaces to compile, and Qt and Fltk had similar stories, whereas reverse-engineering the baroque Yue build (based on Google's internal build systems) was relatively simple. Yue had some sharp edges, but I was able to work around them whilst patiently waiting for patches and fixes upstream.

Immediate mode interfaces were a non-starter for me. For a non-trivial set of otherwise typical controls and window management you have to implement too much yourself, plus being non-native they not only felt wrong (which admittedly is somewhat subjective; the younger crowd seems to think non-native, immediate mode interfaces look more state-of-the-art), but lacked other interfaces for proper desktop integration, like theme change signaling (i.e. notification that a user switch between light and dark modes in the macOS system settings panel).

All-in-all I would highly recommend Yue.

I settled for Yue: https://github.com/yue/yue It's been around for several years. The deciding factor for me was that is has well maintained Lua bindings as part of the core project alongside JavaScript (Node.js) and C++.

I didn't have much luck with libui (crashes, missing features, etc), and various immediate mode alternatives just require too many dependencies and other work that made integration too painful. Plus, Lua bindings for all these were always stale. In fact, Lua binding quality is pretty poor all around including for GTK, Qt, WxWidgets, and FLTK.

There are many more JavaScript developers than C++ developers.

Personally I like Yue, a cross-platform native toolkit library: https://github.com/yue/yue But much of project was already using Lua, so Node.js and Electron were never viable solutions.

A native GUI library https://github.com/yue/yue.

It was a disaster when I announced it on Hacker News, and I got numerous harassments from strangers.

But anyway 2 years since then and I'm still working on it.

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