Data Warehouse Design and Query Performance
Learn how warehouses read less data to answer faster: columnar storage, parallel processing, pruning, partitioning, and clustering, proven with DuckDB.
What You'll Learn
Understanding Why the Same Query Costs 100x Less on One Table Than Another
You are now in the data modeling stage of acme-shop's data journey. You know how to shape tables so the numbers are right.
Placing the Warehouse Among Storage Options
Before looking inside a warehouse, it helps to see where it sits next to the other places data lives.
Separating Storage and Compute
Older warehouses bundled storage and compute on the same fixed hardware.
Understanding Columnar Storage
How data is arranged on disk decides how much a query has to read.
Understanding Massively Parallel Processing
One machine reading billions of rows is slow. Warehouses split the work.
Understanding How the Engine Knows What It Can Skip
This idea ties the whole module together.
Skills You'll Master
Curriculum Index13 topics
Understanding Why the Same Query Costs 100x Less on One Table Than Another
You are now in the data modeling stage of acme-shop's data journey.
Placing the Warehouse Among Storage Options
Before looking inside a warehouse, it helps to see where it sits next to the other places data lives.
Separating Storage and Compute
Older warehouses bundled storage and compute on the same fixed hardware.
Understanding Columnar Storage
How data is arranged on disk decides how much a query has to read.
Understanding Massively Parallel Processing
One machine reading billions of rows is slow. Warehouses split the work.
Understanding How the Engine Knows What It Can Skip
This idea ties the whole module together.
Applying Partitioning and Clustering for Query Performance
Statistics only help when the data is arranged so they are useful. Partitioning and clustering do that arranging.
Loading Data Into a Warehouse
How data arrives affects both cost and layout.
Understanding Query Cost
Performance and cost usually come down to two dimensions, and knowing which one is the problem saves time.
Recognising When a Warehouse Is the Right Tool
You do not need to master every option. The goal is to recognise the shape of the decision.
Hands-On Lab: Diagnose and Fix a Slow Warehouse Table
📌 Remember: This lab is free and runs on your laptop.
Quick Reference
Concepts at a Glance Deciding Where to Look
Common Mistakes
Mistakes to Avoid Assuming no partitioning always means a full scan.
Career Impact
Roles that use the skills in this module.
Data Engineer
Platform Engineer
Next Modules
Related Guides
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
An operational database is built for many small reads and writes, such as placing one order. A data warehouse is built to scan and summarise millions of rows, such as revenue by city. They store and organise data differently because their jobs differ.
Analytical queries usually need a few columns from many rows. A columnar format stores each column together, so the engine reads only the columns the query names. Similar values stored together also compress well.
Pruning means skipping partitions that cannot contain matching rows, using their date range or other statistics. The engine decides this from metadata before reading any data. Less data read usually means a faster and cheaper query.
No. The ideas work the same on a laptop. This module uses DuckDB and Parquet files so you can see the effect of table layout without a cloud account.