Data Warehouse Principles (K15) (Level 5 Data Engineering)

Описание: K15 Made Simple:

Demystifying Data Warehousing | Hungry Now Case Study

What do you do when your company’s data is scattered across Postgres, MongoDB, S3 logs, and GPS streams… and management asks for the average delivery time in Manchester? 🤯

Welcome to the world of data warehousing — the skill that takes messy, chaotic data and turns it into a clean, structured, and trusted source of truth.

In this video, we’ll break down K15: Data Warehousing Principles from the Level 5 Data Engineering standard, using the fictional Hungry Now food delivery company to make every concept real and practical.

What you’ll learn in this video:
🍴 Why we need a data warehouse
The messy “kitchen” vs. the organised “pantry” analogy
Why operational databases (OLTP) ≠ analytics

⭐ Schemas that make analysis simple
The Star Schema explained with Fact Orders + Dim Customer/Restaurant/Driver/Date
The Snowflake Schema trade-off (space vs. complexity)

🛠 Tech stack examples
Ingest → Azure Blob (Data Lake)
Clean + Transform → Azure Data Factory (ETL)
Store → Azure Synapse / Snowflake (Data Warehouse)
Visualise → Power BI / Tableau

📊 Beyond the warehouse
Data Lakes (raw, unstructured) vs. Warehouses (structured, analytics-ready)
Data Marts (team-focused mini-warehouses) for Marketing, Finance, Ops

⚠️ Common mistakes to avoid
Running analytics on production DBs 🚫
Over-engineering schemas
Letting your Data Lake turn into a “data swamp”

🗣 Portfolio-ready ‘I statements’
“I built a star schema that enabled stakeholders to query KPIs by city and time.”
“I integrated a data lake for machine learning alongside a warehouse for BI reporting.”
“I applied data governance to prevent the lake becoming a swamp.”

Chapters
00:00 The messy kitchen problem
01:20 Why a data warehouse is different
02:05 Tools & tech stack
02:34 Star schema explained
03:54 Snowflake schema trade-offs
04:18 Data lakes vs. warehouses vs. marts
05:48 Common mistakes to avoid
06:10 EPA-ready portfolio statements

⚡ Hashtags
#datawarehouse #starschema #SnowflakeSchema #DataEngineering #hungrynow #powerbi #azure #etl #datalake #DataMarts #epaprep #joysofcode

✅ By the end of this video, you’ll know how to turn data chaos into clarity — and how to explain it in a way that impresses EPA assessors, managers, and stakeholders.

So the only question left is… once your data warehouse is built, what’s the first business question you’d ask?