data-engineering-zoomcamp VS Mage

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

In the coming years, Mage will create a cooperative experience so that developers can build data pipelines with their team and level up together. After that journey, Mage will go on an epic quest to create the 1st open world community experience in the data universe.

Link to original blog: https://www.mage.ai/blog/data-sources-ep-2-aws-s3-to-postgres-data-sync-using-singer

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

You're invited to participate in the very first Mage Battlegrounds: Craft insights from real-time customer behavior analysis, a 24-hour virtual hackathon hosted by Shashank Mishra! This data engineering competition will take place on Saturday, April 15, 2023 beginning at 11am (PST). This will be a global event open to all participants who register.

Try this feature: https://github.com/mage-ai/mage-ai/issues/1166

Seeing if we can use it in https://github.com/mage-ai/mage-ai

That being said there’s a solid family of services just breaking ground that make the local pipeline deployment easier (check out https://www.mage.ai, which does have a clear path to cloud deployment of locally developed pipes, it just isn’t well documented yet, and also https://www.neuronsphere.io - which doesn’t have a public solution YET (they’re internally testing an alpha) but they built a cloud deployable solution for their paying customers and working to release one for freemium use)

7️⃣ mage-ai/mage-ai

Yes, check out how Mage does it: https://github.com/mage-ai/mage-ai/tree/master/mage_integrations/mage_integrations/destinations/delta_lake_s3