Data Modeling Revolution: Why Old Rules Are Killing Your Performance

Drowning in data but can't extract what really matters? The problem isn't lack of data - it's how it's structured. Welcome to the hidden world of data modeling - the invisible architecture that makes all your data usable, understandable, and powerful.
But here's the shocking truth: Following traditional data modeling rules from the 90s can actually hurt performance and make your queries slower in modern cloud warehouses. It's time for a revolution.
🔧 What You'll Discover:

Why Kimball's dimensional modeling rules need rethinking in the cloud era
The four fundamental pillars: Grain, Naming, Materialization, and Governance
Star vs Snowflake schemas: When to use each (and why it's not either/or)
How modern ELT pipelines have flipped the old ETL model completely
Why denormalized wide tables often outperform normalized structures today
The art of balancing user needs, performance, and costs across different platforms

💡 Key Modern Principles:
✓ Design for your users AND your technology stack
✓ Materialize strategically for speed and simplicity
✓ Normalize early, denormalize for presentation
✓ Future-proof your models for AI integration
✓ Focus on business requirements over textbook rules
⚡ Platform-Specific Insights:

Redshift (fixed cost) vs BigQuery/Snowflake (dynamic compute) strategies
When wide tables beat perfectly normalized schemas
How column-store databases changed everything
Cost optimization techniques for different warehouse architectures

Timestamps:
0:00 The Data Modeling Revolution
2:24 How Analytics Infrastructure Evolved
4:36 The Four Fundamental Pillars
7:42 Grain: What Each Row Represents
9:18 Naming Conventions That Actually Work
10:30 Materialization: Views vs Tables Strategy
13:00 Permissions and Governance Essentials
14:30 Star vs Snowflake: The Great Schema Debate
18:42 Universal Best Practices
22:00 Future Trends: AI Integration
Real-World Example: One project built a "beautiful textbook star schema" that took ages to create, but when connected to BI tools, the complex joins caused reports to time out completely. Sometimes the "right way" according to old books is the wrong way for modern stacks.
Key Takeaway: By 2025, over 75% of data models will have AI integration. Your models aren't just for human analysts anymore - they're becoming the foundation for machine learning and automated insights.
Whether you're prepping for a data strategy meeting, trying to understand modern analytics, or just curious about how data gets structured behind the scenes, this deep dive gives you the practical knowledge to build models that actually work in today's world.
Subscribe for more insights on data architecture, analytics engineering, and the practical side of modern data strategy!
#DataModeling #DataArchitecture #StarSchema #SnowflakeSchema #DataWarehouse #Analytics #SQL #DataEngineering #BusinessIntelligence #ModernDataStack #CloudDataWarehouse #ELT #DataStrategy

Видео Data Modeling Revolution: Why Old Rules Are Killing Your Performance канала Data-ML Engineer