How to structure data so it is fast, correct and ready for reports — the 4 steps, which Azure service fits which data, star vs snowflake, and the mistakes to avoid.
✍️ Azure Trainings trainers⏱ 4 min read📅 Updated 11 October 2026
Data modelling means deciding how your data is structured, stored and connected — which tables you have, what columns they contain and how they relate. In Azure it also means picking the right service: Azure SQL for transactional apps, Synapse or Fabric Warehouse for analytics, ADLS Gen2 for raw files and Cosmos DB for flexible, global apps. For reporting, use a star schema.
Globally distributed NoSQL data with a flexible schema and low latency.
Model: documents by access pattern
Why it matters
Why data modelling matters
⚡
Performance
Indexing, partitioning and distribution make queries faster and cheaper.
📈
Scalability
A good model grows with your data without slowing down.
✅
Consistency
Keys, constraints and validation stop bad or duplicate data.
🔧
Easy maintenance
Clear, documented models are simpler to change.
📊
Better analytics
Clean, structured data makes reports and ML more accurate.
🔐
Governance
Access control and documentation from day one.
Two approaches
Normalisation vs denormalisation
↔️ Swipe the table sideways
PointNormalisationDenormalisationWhat it doesSplits data into related tablesCombines data into fewer tablesMain goalAvoid duplication, keep data consistentFaster reads and simpler reportsBest forTransactional systems (OLTP)Analytics and reporting (OLAP)Azure exampleAzure SQL Database appSynapse / Fabric Warehouse
🧭 Want to practise modelling a real sales dataset? Our trainers do it step by step in live labs.
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