📐Data Modeling

NEW

Discipline of designing conceptual, logical, and physical representations of data to support integration, storage, and analytics.

DESCRIPTION

Data modeling translates business requirements into structured representations that guide data platform design. It spans conceptual, logical, and physical levels, defining entities, relationships, and constraints. Common approaches include ER modeling for transactional systems and dimensional modeling for BI. Effective modeling enhances data quality, performance, and interoperability, while balancing normalization and denormalization. It underpins reliable ETL/ELT pipelines and sustainable analytics.

ALTERNATIVE DEFINITIONS

IBM

Data modeling is the process of creating a visual representation of a system’s data to communicate connections between data points and structures.

Highlights communication and visualization aspects.

Source

Microsoft Learn

Modeling organizes data into tables and relationships to support analytics and reporting.

BI-centric focus on tables and relationships for analysis.

Source

Databricks

Data modeling is the practice of designing data structures that represent business concepts and support downstream use cases.

Emphasizes alignment to business concepts and use cases.

Source

RELATED TERMS

ER Modeling Kimball Methodology Feature Scaling Star Schema Snowflake Schema UML Schema Design Knowledge Representation Physical Design

COMPETING TERMS

Schema-less NoSQL

RELATIONS

• Narrower: Bitemporal Modeling

KNOWLEDGE AREAS

Data Modeling Data Architecture Data Warehousing & BI Software Modeling

TARGET AUDIENCE

Data Modeler Data Architect Analytics Engineer Data Engineer BI Specialist Information Analyst

EXTERNAL SOURCES

• Wikipedia - Data modeling
• IBM - Introductory guide to data modeling
• Microsoft - Modeling in Power BI explained
• Kimball Group - Dimensional modeling best practices

TOP QUESTIONS

What is the goal of Data Modeling?

To translate business requirements into structured data designs that enable reliable integration, efficient storage, and performant analytics.

Category: Overview

When should you prefer dimensional modeling?

Use dimensional models for analytics and reporting, optimizing aggregations and readability with facts and conformed dimensions.

Category: Opinion

What are common Data Modeling pitfalls?

Ignoring business semantics, over-normalizing, poor naming, and inconsistent keys lead to confusion and performance issues.

Category: Mistakes

How does Data Modeling relate to schema-on-read?

Schema-on-read delays structure decisions, but modeling still matters to define semantics and reusable views for analysis.

Category: Comparison

What documentation supports Data Modeling?

ER diagrams, model glossaries, change logs, and lineage documentation help maintain clarity and governance.

Category: Management

How does modeling impact ETL/ELT design?

Models define transformations, keys, and constraints, guiding pipeline logic, dependencies, and data quality checks.

Category: Requirements

What’s hot in Data Modeling today?

Semantic layers and metrics stores formalize business logic, enabling consistent analytics across tools and domains.

Category: Topic

How do you validate a Data Model?

Conduct stakeholder reviews, prototype queries, test integrity constraints, and monitor performance under realistic workloads.

Category: Validation

Where to learn Data Modeling?

Study ER and dimensional modeling, build sample models, and explore Kimball resources and vendor documentation.

Category: Learning