Databases & the SQL Landscape

Here's everything we covered in our Second session together.

Part 1

What is Data?

We covered what data is, how it gets generated, and why it lives in memory before it ever reaches a database — and what happens when it doesn't make it.

Part 2

Operational Databases & ACID

We explored why operational databases exist and unpacked ACID — the four rules that stop two people withdrawing £1,000 each from an account with only £1,000 in it, at the same time, from different locations.

Part 3

How Data is Structured

We looked at the flat approach — and why it breaks down. Then we walked through normalisation: how organising data properly leads to cleaner, more reliable systems. We also covered OLTP and why developers write SQL.

Part 4

The Data Warehouse

We covered why businesses need a separate system for analytics, the difference between batch, real-time, and mirroring approaches, and what OLAP means — and why modelling for a warehouse is a completely different discipline.

Part 5

Data Modelling in Practice — With AI

We went from concept to physical tables, looked at what bad modelling looks like in the wild, and practised validating AI-generated data models — because AI gets this wrong more often than it admits.

Part 6

SQL: The Language of Data

We finished by covering why SQL exists, how it's structured, and — crucially — how databases actually execute your queries. Hint: it's not in the order you write them.