The Short Version

Data architecture is the big picture - how data flows across your organization, which systems exist, how they connect, and what standards apply.

Data modeling is the detail - how individual datasets are structured, what fields exist, how tables relate to each other.

Architecture decides you need a data warehouse. Modeling decides how tables inside that warehouse are organized.

Both matter. They operate at different levels of abstraction and require different skills.

The Key Difference

Scope

Architecture answers organizational questions:

  • What systems do we need?
  • How does data flow between them?
  • Who owns what?
  • What standards apply across the platform?

Modeling answers structural questions:

  • What tables should exist?
  • What columns belong in each table?
  • How do tables relate to each other?
  • How should we organize facts and dimensions?

Analogy

Think of building a city:

  • Architecture is city planning - where neighborhoods go, how roads connect them, where utilities run
  • Modeling is building design - how rooms are arranged inside each building, what goes where

You need both, but they’re different disciplines.

What Data Architecture Covers

Architecture operates at the system level:

Platform Decisions

  • Data warehouse vs data lake vs lakehouse
  • Which cloud provider and services
  • Real-time vs batch processing
  • Build vs buy for each component

Integration Patterns

  • How data moves between systems
  • ETL vs ELT approaches
  • API patterns and contracts
  • Event-driven vs scheduled pipelines

Governance Framework

  • Data governance policies
  • Access control patterns
  • Quality standards
  • Compliance requirements

Organizational Structure

  • Team boundaries and ownership
  • Centralized vs federated approaches
  • How domains are divided

For more detail, see What Is Data Architecture?

What Data Modeling Covers

Modeling operates at the dataset level:

Schema Design

  • Table structures and relationships
  • Column definitions and data types
  • Primary and foreign keys
  • Constraints and validation rules

Modeling Patterns

  • Dimensional modeling - Star schemas with fact and dimension tables
  • Data vault - Hub, link, and satellite tables for flexibility
  • Third normal form (3NF) - Normalized structures for transactional systems
  • Wide tables - Denormalized for specific analytics use cases

Naming and Documentation

  • Consistent naming conventions
  • Field descriptions and business definitions
  • Lineage within datasets

Performance Optimization

  • Indexing strategies
  • Partitioning schemes
  • Clustering and sorting
  • Materialized views

How They Relate

Architecture Contains Modeling Decisions

Architecture defines the framework within which modeling happens:

  • Architecture says “we use Snowflake with a dimensional model”

  • Modeling defines the specific star schemas within Snowflake

  • Architecture says “Bronze/Silver/Gold layers”

  • Modeling defines table structures at each layer

  • Architecture says “domain-owned datasets”

  • Modeling defines schemas within each domain

Modeling Informs Architecture

Modeling requirements can drive architectural decisions:

  • Complex modeling needs might require specific platform capabilities
  • Performance requirements might influence architecture choices
  • Governance requirements affect how models are designed

The Feedback Loop

Good architecture makes modeling easier by providing:

  • Clear standards to follow
  • Appropriate tools for the job
  • Defined boundaries and ownership

Good modeling validates architecture by showing whether:

  • The platform supports needed structures
  • Performance meets requirements
  • Standards are practical to follow

Common Confusion Points

“We need better data modeling” vs “We need better architecture”

Symptoms that suggest modeling problems:

  • Individual tables are poorly structured
  • Queries against specific datasets are slow
  • Business definitions are inconsistent within a domain
  • Analysts struggle to join related tables

Symptoms that suggest architecture problems:

  • Data is scattered across disconnected systems
  • The same data exists in multiple places with different values
  • Nobody knows how data flows through the organization
  • Adding new data sources requires reinventing the wheel

Often, what looks like a modeling problem is actually an architecture problem. If every team models data differently because there are no standards, that’s architecture.

Role Confusion

Data architects focus on system-level design:

  • What Is a Data Architect?
  • Senior role, 10+ years experience typical
  • Works across teams and stakeholders
  • Concerned with platforms, integration, governance

Data modelers focus on dataset-level design:

  • Often part of data engineering or analytics teams
  • 3-10+ years experience typical
  • Works within specific domains or projects
  • Concerned with schemas, relationships, performance

Some people do both. In smaller companies, the same person often handles architecture and modeling. But they’re distinct skills.

When You Need Each

You Need Architecture When

  • You’re building or redesigning your data platform
  • Multiple teams need to share data
  • You’re evaluating major technology changes
  • Data is scattered and nobody owns the big picture
  • Cloud costs are growing without clear value

You Need Modeling When

  • You’re designing a new dataset or domain
  • Existing models are causing performance problems
  • Business definitions are inconsistent
  • Analysts can’t find or understand the data they need
  • You’re implementing a specific analytics use case

You Need Both When

  • You’re building a data platform from scratch
  • You’re modernizing a legacy system end-to-end
  • You have architectural direction but no one to implement models
  • Models exist but don’t fit together coherently

Modeling Within Architecture

Good architecture defines how modeling should happen:

Standards

Architecture should specify:

  • Which modeling patterns to use (dimensional, data vault, etc.)
  • Naming conventions for tables, columns, schemas
  • Documentation requirements
  • Testing and validation standards

Boundaries

Architecture should define:

  • Which domains own which data
  • Where transformation happens (Bronze → Silver → Gold)
  • How shared entities are modeled across domains
  • What interfaces exist between domains

Tools

Architecture should provide:

  • Platforms that support needed modeling patterns
  • Tooling for model documentation and discovery
  • Testing frameworks for model validation
  • Environments for model development

The Layered Approach

Many organizations separate architecture and modeling responsibilities:

Architecture Team/Role

  • Defines platform and standards
  • Reviews cross-domain modeling decisions
  • Ensures consistency across the organization
  • Handles integration and governance

Domain/Feature Teams

  • Implement models within their area
  • Follow architectural standards
  • Own quality within their domain
  • Propose changes when standards don’t fit

This separation works because:

  • Architecture decisions are infrequent but high-impact
  • Modeling decisions are frequent but localized
  • Different skills are emphasized at each level

Getting Help

If you’re not sure whether you have an architecture problem or a modeling problem, architecture advisory can help assess your situation and determine the right approach.

For ongoing architecture direction, a fractional data architect provides senior guidance without a full-time hire - including establishing modeling standards that teams can follow.