SQL vs. NoSQL:

After completing my database course with a 4.0 GPA and reading multiple articles, I still need to revise SQL vs. NoSQL before interviews. My notes have depth, but before an interview, you need breadth. You might argue I can use an LLM or chatbot for this, but I prefer to write for myself. Anyway, skipping the yapping and jumping straight into the actual write.

The Big Question

They always ask: “Why would you pick one over the other?”

And I can't just say, “SQL is for banks, NoSQL is for startups.”

So here's the real framework:

SQL

• Structured, rigid, ACID-compliant.
• Tables, schemas, relationships, foreign keys, joins (that make you cry).

Use it when:

• Your data is predictable.
• Your queries are complex (think multiple aggregations, window functions, joins across 6 tables).
• You absolutely cannot lose a single write (finance, inventory, ledgers).

NoSQL

• Flexible, scale-out, BASE (Basically Available, Soft state, Eventual consistency).
• Collections/documents, key-value, wide-column, graphs; schemas are optional (well, they shouldn't be optional, but it's fine for now).

Use it when:

• You have billions of reads/writes.
• Data shape may change every sprint.
• You’re storing JSON blobs (user sessions, product catalogs, IoT logs).

1. Architecture (Relational vs. Non-Relational)

Don’t just say:

• SQL = tables, rows, columns, keys; everything connected like a structured tree.
• NoSQL = documents, key-value, graphs; data in its chaotic natural state.

Interview Hack: When they ask, don’t just state this. Say:

“SQL is like a spreadsheet on steroids with foreign keys. NoSQL is like a JSON dump that grew up and got a job.”

Then tie it to a use case:

• If my data has clear relationships (like customers to orders), I go SQL.
• If each record is a snowflake (like IoT sensor readings), I go NoSQL.

2. Schemas & Query Languages (Fixed vs. Dynamic)

• SQL forces you to define your schema before you write a single row. Changing it later? That’s a migration nightmare.
• NoSQL lets you add fields—each document can be its own vibe.

Question: But isn’t dynamic schema risky?

Interview Hack: Own it and say:

“Yeah, dynamic schemas can hurt you later if you're not careful, but for MVPs and rapid prototyping, that flexibility is a superpower. You ship fast, figure out the data shape as you go, and refactor. I’d use SQL when my data model is stable, and NoSQL when I’m still figuring out what fields I need.”

Bonus points: Mention PostgreSQL with JSONB as a hybrid solution that gives you the best of both.

3. Scaling (Vertical vs. Horizontal)

• SQL scales up (vertical).
• NoSQL scales out (horizontal).

Interview Hack: Draw the picture with your hands:

• Vertical (SQL) = taller tower.
• Horizontal (NoSQL) = wider village.

Then say:

“For a media platform storing millions of videos, vertical scaling hits a wall fast. That’s when I’d shard across NoSQL nodes.”

4. Data Structure (Table-based vs. Document)

• SQL = every row has the same columns.
• NoSQL = you can have nested arrays, missing fields, or totally different shapes per record.

Interview Hack:
Use the product catalog example:

“A laptop has CPU and RAM; forcing both into the same SQL table is painful. A document DB like MongoDB handles that natively.”

That’s a concrete, relatable example.

The 3-Second Differentiation

Quick-Fire Mental Checklist

• Do I need ACID? → SQL
• Do I need dynamic fields per record? → NoSQL
• Do I have more reads than writes? → Both, but cache with NoSQL.
• Do I have more writes than reads? → NoSQL (especially time-series).
• Is my team SQL-savvy? → Don’t force NoSQL just for hype.
• Am I building an MVP? → NoSQL for speed, but plan the migration to SQL later.