Tag:data modeling

Articles tagged "data modeling".

• 6 MIN READ•Feb 18, 2026

  What Is Data Modeling? Definition, Types, and Examples

  Data modeling defines the entities, attributes, and relationships in your data. Learn the conceptual, logical, and physical levels and the main techniques.

  data modelingdata engineeringdata lakehouse
• 6 MIN READ•Feb 18, 2026

  Conceptual vs Logical vs Physical Data Models (Examples)

  Conceptual, logical, and physical data models explained with one e-commerce example: what each level contains, who owns it, and how one feeds the next.

  data modelingdata engineeringconceptual model
• 6 MIN READ•Feb 18, 2026

  Star Schema vs Snowflake Schema: Differences and When to Use

  A star schema keeps each dimension in one flat table; a snowflake schema splits dimensions into normalized sub-tables. Compare joins, speed, and use cases.

  data modelingdata engineeringstar schema
• 6 MIN READ•Feb 18, 2026

  Dimensional Modeling Explained: Facts, Dimensions, Grain

  Dimensional modeling organizes analytics data into fact tables and dimension tables. Learn how to declare the grain, design both table types, and conform them.

  data modelingdata engineeringdimensional modeling
• 6 MIN READ•Feb 18, 2026

  Denormalization: When and Why to Denormalize Your Data

  Denormalization adds deliberate redundancy to cut joins and speed up reads. Learn when it helps analytics, when to avoid it, and how views do it virtually.

  data modelingdata engineeringdenormalization
• 5 MIN READ•Feb 18, 2026

  Data Modeling Best Practices: 7 Mistakes to Avoid

  Seven data modeling mistakes and fixes: undefined grain, cryptic names, skipped conceptual models, over-normalizing, thin docs, and ignored governance.

  data modelingdata engineeringbest practices
• 5 MIN READ•Feb 18, 2026

  Data Vault Modeling: Hubs, Links, and Satellites

  Data Vault modeling explained: Hubs hold business keys, Links hold relationships, and Satellites hold history, plus how to expose Data Vault data for analytics.

  data modelingdata engineeringdata vault
• 5 MIN READ•Feb 18, 2026

  Data Modeling for Analytics: Optimize for Queries

  Why analytics needs a different data model than your application: denormalization, wide tables, and pre-aggregation patterns that make analytical queries fast.

  data modelingdata engineeringanalytics
• 5 MIN READ•Feb 18, 2026

  Slowly Changing Dimensions: Types 1-3 with Examples

  Slowly Changing Dimensions Types 1, 2, and 3 explained with examples: overwrite, add a history row, or add a column, plus how to choose the right SCD type.

  data modelingdata engineeringslowly changing dimensions
• 5 MIN READ•Feb 18, 2026

  Data Modeling for the Lakehouse: What Changes

  How data modeling changes in a lakehouse: schema-on-read, Iceberg partition evolution, and a virtual Medallion Architecture built as SQL views over open tables.

  data modelingdata engineeringdata lakehouse
• 8 MIN READ•Oct 30, 2024

  Data Modeling - Entities and Events

  The difference between modeling data as entities and as events, when each approach fits, key design choices, and how to combine both for full analysis.

  data lakehousedata engineeringdata modeling