Why is modern data architecture so confusing? And what made sense for me
3 comments
·September 22, 2025willvarfar
piva00
Hilarious how true this can be, at some point I worked at a place that had three different competing setups for data workflows, with completely different stacks in all the possible ways: different programming languages, data stores, pipeline orchestrators, etc.
An absolute mess of technologies that no single person could make sense, backfilling when something went wrong could need 5-10 people to coordinate.
The running joke was that the data engineering department was trying to compete with the frontend devs on how fast they could throw a whole architecture out for a new fad.
chauhanbk1551
I’m a data engineering student who recently decided to shift from a non-tech role into tech, and honestly, it’s been a bit overwhelming at times. This guide I found really helped me bridge the gap between all the “bookish” theory I’m studying and how things actually work in the real world. For example, earlier this semester I was learning about the classic three-tier architecture (moving data from source systems → staging area → warehouse). Sounds neat in theory, but when you actually start looking into modern setups with data lakes, real-time streaming, and hybrid cloud environments, it gets messy real quick.
I’ve tried YouTube and random online courses before, but the problem is they’re often either too shallow or too scattered. Having a sort of one-stop resource that explains concepts while aligning with what I’m studying and what I see at work makes it so much easier to connect the dots.
Sharing here in case it helps someone else who’s just starting their data journey and wants to understand data architecture in a simpler, practical way.
Real medium and large companies are so much messier. Almost guaranteed to have different iterations of each architecture and multiple competing architectures all running in parallel, with divided siloed and opposing ownership and perverse incentives and all the rest. Show me the spaghetti dataflow chart of an org and I will reverse-engineer the history of power struggles, resume-engineering and fads and failures that created it :)