In this era of rapid AI hype, learning often feels… obsolete.
Especially when it comes to “learning how to code.”
“Learning to code has become optional” — but learning fundamentals and how things work under the hood is more important than ever.
Imagine you’re building a 100k MMR SaaS
(…of course, with zero bugs 😂)
and you don’t know which database to choose.
There are tons out there:
- PostgreSQL – flexible queries, great for read‑heavy workloads
- Cassandra – built for write‑heavy, no‑relational data
- MongoDB – schema‑flexible, good for raw or vector‑like data
- Redis – in‑memory, volatile, perfect for caching
- Elasticsearch – search‑first, as the name suggests
But here’s the catch:
You don’t need all of them.
You don’t even need one “perfect” database.
It’s always about your use case.
How do you know which one to pick?
How do you choose the right architecture?
A Real‑World Mistake
While building an e‑commerce store, I decided to use all five of these.
AI was all in.
The idea sounded “scalable.”
The harsh reality?
At 10K users, a single PostgreSQL instance alone could’ve handled everything.
I learned it the hard way.
You can learn it the smart way.
Comment “Databases” and I’ll send you a short list of resources (articles, docs, and patterns) that helped me clean up that mess.
So, How Do You Decide on a Database?
- Start with reads vs writes.
- Ask: “Do I need relations or not?”
- Ask: “Is this data transient, cached, or mission‑critical?”
- Then, and only then, pick the tool.
The point isn’t to master every database.
It’s to understand enough fundamentals that you can stop over‑engineering and start shipping.
What’s your “I over‑engineered this and learned the hard way” story with databases or architecture? Drop it in the comments 👇
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