Hi,

I loved the idea and the content. I would like to ask if you can open source the webpage code so anyone can fork it and re-create this page with a different perspective.

Thanks

Hi,

First of all, congratulations on the awesome work. I have been in the Data engineering space for some time and this is one of the best written roadmaps / guides to learning the area! It is both excellent in content and appealing in presentation.

I did have a couple of suggestions though where I felt it was missing some key resources and wanted to see if you like the idea, I can submit a PR.

• Any discussion of Data Engg today without reference to offerings in the 3 major Cloud providers or other related cloud technologies in the space (such as Amazon Glue, Apache Beam, etc.) seems incomplete
• A touch of the Periscope, Mode, Looker, Superset world would also help complete the picture since the line between these and Data Engg is sometimes blurry
• No reference to modern ETL / ELT tooling such as dbt, Stitch, Fivetran etc.?
• Touching on data storage formats - Avro, Thrift, Parquet
• Mention of Pulsar
• Mention of Prefect, Dagster as upcoming challengers to Ariflow

Thanks!

Hi, this is good work appreciated by those like me who looks for an advanced material.
I suggest that you extend this project to cover other appealing and non obvious paths such as Software Engineering, Security Analyst, AI Expert..etc
Thanks !