Database Design vs Data Warehouse Design in the Age of AI

By a lad who’s seen enough schema spaghetti to know better

Introduction: Why This Still Matters

You’d think in 2025 we’d have this sorted. Databases? Warehouses? Just toss your data into the cloud and let AI sort it, right? Stall the ball , it's not that straightforward.

The truth is, even in the age of AI, how we design our data systems and why, still matters more than ever.

Not just for performance, but for clarity, governance, and not losing your mind when a query takes 18 minutes because someone forgot to apply indices.

So, what’s the real difference between database design and data warehouse design? And how is AI turning both inside out? Grab a cup of tea (or whatever you’re having yourself) and let’s break it down.

Part 1: The Quick and Dirty Breakdown

Database Design (OLTP)

Think: apps, transactions, CRUD. You’re designing the engine room.

• Highly normalised: You split data into logical tables to reduce redundancy.

• Focused on integrity and speed for short, high volume operations.

• Designed for real time, operational use.

• Examples: User accounts, order systems, payment logs.

If a database were a pub... it’s the bar counter, everything has a place, and it needs to move fast and stay clean.

Data Warehouse Design (OLAP)

Think: analytics, dashboards, big picture decisions. You’re building the control tower.

• Largely denormalised: Star or snowflake schemas for query performance.

• Designed for batch or real time analytics, not fast transactions.

• Stores historical, aggregated data.

• Examples: Sales trends, customer behaviour, operational KPIs.

If a warehouse were a pub… well, it’s the back room where someone’s trying to work out if Guinness sales are up this quarter.

Part 2: Same Data, Different Problems

Here's the trick: both systems might use similar data, but the goals are wildly different.

Design Goal	Database	Data Warehouse
Data Structure	Normalised	Denormalised
Performance Goal	Write & update speed	Query & read speed
Use Case	Operational tasks (apps)	Analytical tasks (insights)
Flexibility	Rigid but clean	Flexible but wide
Common Mistake	Over normalising	Under modelling dimensions

Phase 3: What's AI doing to both (for better or worse)?

Here’s where things get interesting, AI isn’t just helping, it’s redefining the rules.

AI in Database Design

• Schema suggestion: Some AI tools can generate schemas from user stories or sample data.

• Anomaly detection: AI can flag odd patterns in transactional data (before your customers do).

• Auto indexing: Tools like PostgreSQL's AI assisted tuning can recommend indexes based on live queries.
  

But here’s the thing, AI doesn’t know your business logic. It’ll happily optimise a schema that breaks your billing workflow because it saved 0.3ms on a SELECT.

Translation? You still need a sharp human eye. AI’s great craic, but it has the common sense of a wet brick sometimes.

AI in Data Warehouse Design

• Metadata driven automation: Tools like dbt and Informatica use AI to build and maintain models dynamically.

• AI powered transformation pipelines: ELT workflows are getting smarter adapting based on usage.

• Pattern based query optimisation: Warehouses like Snowflake and BigQuery learn from your queries to improve performance.

AI makes warehousing slick, but messy source data in = nonsense insights out. You still need someone to ask, “Wait, does this KPI even make sense?”

Part 4: What Should You Be Doing Differently

If you’re a senior dev or architect, here’s where your head should be:

• Understand both systems: You can’t design apps or analytics in isolation anymore.

• Expect constant change: AI driven tooling means schemas evolve faster, plan for agility, not perfection.

• Stay human led: AI can model your data, but it can’t model your domain knowledge. Yet.
  

And for God’s sake, write documentation. AI might guess what you meant. Your teammates won’t.

A Final Word (and a Word to the Wise)

Database design vs data warehouse design used to be two very separate skillsets. Now, they’re part of the same puzzle, with AI shaking the box.

You don’t have to become an expert in everything, but you do need to ask smarter questions:

• What data matters?

• Who's using it?

• Can AI help, or are we just automating bad decisions?
  

So next time someone says, “Just stick it all in Snowflake and let the models figure it out,” you can smile politely and say, “Ah here, have you tried not making a dog’s dinner of it?”

TL;DR (For the Skimmers in the Back)

• Database = Operational. Warehouse = Analytical.

• Normalisation = integrity. Denormalisation = speed.

• AI = a helpful eejit, brilliant for performance, clueless about context.

• You = still essential. Don't let the robots fool ya ;)