Cookbook VS data-engineering-zoomcamp

https://github.com/andkret/Cookbook arunca un ochi aici. Omul are si youtube channel https://www.youtube.com/@andreaskayy

I can recommend https://learndataengineering.com by Anreas Krenz. Will guide you via all important topics starting from sql & python to building pipelines using AWS/GCP. I used to participate for 1 year (costs ~ 200 Euro/220$). It's a self-paced. So for ~15h/week you can switch into DE position for appr. 6 months.

I wonder if anyone involved in this post and comments have tried this? https://learndataengineering.com/

I think it's allowed: https://learndataengineering.com

From your post, you seem to be trained for data science for physics modeling, so I'd recommend to get started with https://ml-ops.org/ and for the data engineering part, I found this https://github.com/andkret/Cookbook open source cookbook to be invaluable.

I am doing this currently to fill in the blanks: https://learndataengineering.com. Also, do you know Python? If not take class on Udemy on that. Finally, data engineering is all about tools these days. I saw someone recommended this book here: Data Engineering with Python, I find it super hopeful. You download these tools (Apache Airflow, etc) and get a go with it. I am going to build some data pipelines via this book :)

the best way to be a dev or DE is to build stuff, not learning about algorithms. Just google DE academy, bootcamp or so. The linked one is quite good for a cheap price. A degree prepares you mostly for a PhD, not for a job. So dont look for degrees preparing you for a job in general.

I usually don't do self promotion, but because you directly asked for a good source. Look at my academy: https://learndataengineering.com

Find something like https://learndataengineering.com/, udemy or any other 'bootcamp/course' that goes on for few months and learn it. It is important that you will have some mentors or study buddies to exchange ideas or so.

References: Data engineering zoomcamp week 6 course and homework notes: https://github.com/DataTalksClub/data-engineering-zoomcamp/tree/main/cohorts/2024/06-streaming

dbt is the main part of my data engineering project for Data Talks Club's data engineering zoomcamp. After a few frustrating errors on my part, I finally figured out how to make models, where to put the staging models and where to put the core models, how to compile a seed file, and how to join it to the main file in order to produce data for visualization. I also used the git interface to continually upgrade my repository. This was extremely convenient and helpful.

For Week 4 of DataTalksClub's data engineering zoomcamp, we had to install dbt and create a project. This was a formidable task. dbt is a data transformation tool that enables data analysts and engineers to transform data in a cloud analytics warehouse, BigQuery in our case. It took me a very long time to do this, and in this case I needed the homework extension.

In this video we learned how to test and document dbt models. We also learned about the codegen library. This is part of Week 4 of the data engineering zoomcamp by DataTalksClub.

If you want to run these commands yourself, either in a Jupyter notebook or in Google Colab, you can get the file from HERE. You can get an overview of the workshop HERE. When I ran in a Jupyter notebook, I had to delete the first line (%%capture) and put quotes around dlt[duckdb] in the second line.

Take a look.DE zoomcamp

If you need guidance you can attend the data engineering zoomcamp, it's free and quite solid.

Welcome. Start with Data Engineering Zoomcamp, try and build a project, see if you like it, then continue to get into deeper resources.

Can take a look at this - https://github.com/DataTalksClub/data-engineering-zoomcamp

I think you can start with something free with this pretty practical course on Data Engineering from DataTalksClub - https://github.com/DataTalksClub/data-engineering-zoomcamp