data_engineering_on_gcp_book
A book describing how to set up and maintain Data Engineering infrastructure using Google Cloud Platform. (by Nunie123)
check-all-the-things
check all of the things! (by collab-qa)
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| data_engineering_on_gcp_book | check-all-the-things | |
|---|---|---|
| 12 | 3 | |
| 116 | 44 | |
| - | - | |
| 2.6 | 5.3 | |
| about 3 years ago | 3 months ago | |
| Python | ||
| - | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
data_engineering_on_gcp_book
Posts with mentions or reviews of data_engineering_on_gcp_book.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-01.
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How possible is it for a beginner to establish pipelines, data warehouse, and visualization solution as a team of 1?
This book will walk you through setting up a complete data engineering stack on GCP: https://github.com/Nunie123/data_engineering_on_gcp_book
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Python & SQL knowledge needed for ETL?
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.
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Learning Python and SQL: What should be my next step?
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
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Github Repo with All Data tranformation,Cleaning,Validation
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
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What is the low hanging fruit for a brand new GCP data engineer to learn?
Check out this book: https://github.com/Nunie123/data_engineering_on_gcp_book
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Unsure about overall process of data engineering
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
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[HELP] Airflow Reverse proxy + load balancer +docker
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
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Transition from a Quality engineer to Data engineer
This book might be a good resource for you: https://github.com/Nunie123/data_engineering_on_gcp_book
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Accepted a data engineer intern role at a Big N company - how do I learn as much as possible?
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.
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What tools, software, programming languages, and etc. does a data engineer need to have in 2021
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
check-all-the-things
Posts with mentions or reviews of check-all-the-things.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-04.
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Golang Security Checker
Some links on these pages:
https://analysis-tools.dev/tag/rust https://github.com/mcandre/linters#rust https://github.com/collab-qa/check-all-the-things/blob/maste...
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Ask HN: What are some tools / libraries you built yourself?
Myself and someone else built check-all-the-things, a tool that makes it easier to run various static analysis tools and other checks on a directory.
https://github.com/collab-qa/check-all-the-things
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What is your “I don't care if this succeeds” project?
https://github.com/collab-qa/check-all-the-things/
What are some alternatives?
When comparing data_engineering_on_gcp_book and check-all-the-things you can also consider the following projects:
shotcaller - A moddable RTS/MOBA game made with bracket-lib and minigene.
snipp.in - Fast, Light-weight, Notes, Snippet manager and code editor directly inside your browser
FactGraph - FactGraph monorepo (backend + frontend + landing page + blog)
ddt - Golang Dynamic Decision Tree
beubo - Beubo is a free, simple, and minimal CMS with unlimited extensibility using plugins
meal-scheduler
distribyted - Torrent client with HTTP, fuse, and WebDAV interfaces. Start exploring your torrent files right away, even zip, rar, or 7zip archive contents!
gosec - Go security checker
go-plugin - Golang plugin system over RPC.
dali - Indie assembler/linker for Dalvik VM .dex & .apk files (Work In Progress)
Nullboard - Nullboard is a minimalist kanban board, focused on compactness and readability.
data_engineering_on_gcp_book vs shotcaller
check-all-the-things vs snipp.in
data_engineering_on_gcp_book vs FactGraph
check-all-the-things vs ddt
data_engineering_on_gcp_book vs beubo
check-all-the-things vs meal-scheduler
data_engineering_on_gcp_book vs distribyted
check-all-the-things vs gosec
data_engineering_on_gcp_book vs go-plugin
check-all-the-things vs distribyted
data_engineering_on_gcp_book vs dali
check-all-the-things vs Nullboard