I’m quite new to data engineering , have a possible dumb question about data warehousing. I learned a lot initially about schema design to the 3NF but I’ve been reading books on facts and dimensions. Question: is loading data initially into 3NF structure and then afterwards Denormalizing a bad idea?

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Ok first of all there’s lot going on there and it feels like you are complicating unnecessarily. Ideally denormalized to normalized is the approach but rev could happen if necessary, but I don’t see the reason to do so.


Everyone starts somewhere man, if data size really dosent matter I’m going to download 5-10gb and start learning from books. I don’t exactly have a great environment in my workplace to be getting any more information other than reading the code that’s already written. No tickets . Documentation

The second half of your penultimate comment totally put me into confusion as I could barely understand what you are trying to say. There’s no right or wrong approach. Just make sure whatever you are planning, keep your manager in loop on your ramping up approaches. Your supervisor knows the workflow much better as every company has their own tailor fit way of doing and they can guide you better if you ever were to run into pitfalls. If they don’t know any once of it then it’s time to take matter in your own hands and start writing up some onboarding docs and get a supervisor stamp on it. It will leave a long lasting impression and helps you grow up the ladder including $$$.

My 2c to you. I sympathize your situation if there’s no mentor in your team because if anything goes wrong then saying “Im sorry” will be too late. Be careful and test your content prior to deployment.

A few suggestions which I'm highly biased here.

1. You can find a high level DE roadmap her e- https://github.com/datastacktv/data-engineer-roadmap. Now don't get overwhelmed by the sheer size of tech stack but be sure to master at least the bare min. concepts like Data Structures and Algorithms, Java/Python/(or any OOPs), SQL, Performance Tuning, Pipeline Building, Indexing, Sharding, Partitioning, Bucketing, etc.

2. Here's a good post I suggest to noobs (below). You don't have to pay for videos but there're already enough MOOCs available for mastering domain concepts. They also suggest how to get your hands deep into code.
a. https://medium.com/coriers/data-engineering-roadmap-for-2021-eac7898f0641
b. https://understandingdata.com/the-data-engineers-roadmap/

3. Take advantage of your workplace Saas, Paas, or Iaas to deal with real life business problems and if needed get approval from your supervisor.

4. Unless you aren't the only DE, see if you can talk to your peers and understand contributed to the team.

Good luck

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