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Data Modelling Fundamentals

Build Solid Data Foundations

💡 Hover over any tip or practice to see practical examples
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Entities & Attributes

  • Identify business objects and concepts
  • Define key attributes for each entity
  • Keep it high-level and business-focused
  • Use clear, meaningful names
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Cardinality & Keys

  • One-to-One, One-to-Many, Many-to-Many
  • Define primary keys uniquely
  • Use foreign keys for relationships
  • Establish referential integrity
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Normal Forms

  • 1NF: Eliminate repeating groups
  • 2NF: Remove partial dependencies
  • 3NF: Eliminate transitive dependencies
  • Balance normalization with performance
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Schema Design

  • Star schema for analytical workloads
  • Snowflake schema for normalized dimensions
  • Consider indexing strategies
  • Plan for scalability and growth
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Choose Wisely

  • Match data types to actual data
  • Consider storage and performance
  • Use appropriate precision and scale
  • Plan for future data growth
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Data Integrity

  • NOT NULL for required fields
  • UNIQUE for distinct values
  • CHECK constraints for validation
  • Default values when appropriate

Modelling Best Practices

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Start with Business Requirements
Understand business processes and needs before designing. A good model reflects how the business operates.
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Document Everything
Maintain clear documentation of entities, relationships, and business rules for future reference and maintenance.
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Keep It Simple
Avoid over-engineering. The best models are simple, intuitive, and easy for others to understand and use.
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Plan for Change
Design flexibility into your model. Business requirements evolve, and your schema should accommodate changes.
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Balance Performance & Normalization
Sometimes denormalization improves query performance. Know when to break normalization rules strategically.
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Validate with Sample Data
Test your model with real or realistic data early to identify issues before full implementation.