dbterd

Generate ERD-as-a-code from your dbt projects

Transform your dbt artifact files or metadata into stunning Entity Relationship Diagrams using multiple formats: DBML, Mermaid, PlantUML, GraphViz, D2, DrawDB, and a canonical JSON payload

🚀 Quick Start

Two ways to get your first ERD on screen — pick whichever matches your mood:

🖱️ Prefer a GUI? Install the dbterd for VS Code extension, open your dbt project, and explore interactive ERDs without touching the command line.

⌨️ Prefer the CLI?

pip install dbterd --upgrade
dbterd run -ad samples/dbtresto

That's it — your diagram is generated. Read on for the full tour.

🎯 Entity Relationship Detection

dbterd intelligently detects entity relationships through three algorithms — pick the one that matches how your dbt project expresses its data contracts:

• 🧪 Test Relationships (default) — infers relationships from dbt relationships data tests
• 🏛️ Semantic Entities (-a entity_relationship) — detects relationships via dbt Semantic Layer entity definitions
• 📜 Model Contract Constraints (-a model_contract) — detects relationships via dbt model contract's foreign_key constraints (dbt 1.9+ / manifest v12+)

For detailed configuration options, see our CLI References.

🎨 Supported Output Formats

No need to pick just one — dbterd has a format for every occasion, from quick GitHub previews to full-blown interactive database designers.

🎯 Try the DBML Demo, or see the canonical JSON payload that powers the VS Code extension and docs sites!

🌟 Ecosystem

dbterd plays well with others. These companion projects build on top (and "down")of it to take your ERDs beyond the command line:

Project	What it does
	dbterd-vscode — a VS Code extension that turns your dbt project into interactive ERDs without ever leaving your editor, powered by dbterd under the hood.
	dbdocs — an alternative dbt docs site — catalog, ERD, and column-level lineage baked into a single self-contained HTML file.
	artifact-parser — a pluggable framework that converts JSON artifacts into typed, validated Python objects

🚀 Installation

Requires Python 3.10+. dbterd 1.25 was the last release to support Python 3.9; support was dropped since 1.26, as 3.9 reached end-of-life in October 2025 and the dbt 1.11 artifact parser emits X | Y type annotations that won't evaluate on it anyway. If you're still on 3.9, pin dbterd==1.25.* — otherwise upgrade your interpreter (it's worth it).

pip install dbterd --upgrade

Verify Installation:

dbterd --version

pip install "artifact-parser[dbt]" --upgrade

⚙️ Configuration Files

Tired of typing the same CLI arguments repeatedly? Your fingers deserve better. dbterd supports configuration files to streamline your workflow!

# Initialize a configuration file
dbterd init

# Now just run with your saved settings
dbterd run

Supported formats:

• .dbterd.yml - YAML configuration (recommended)
• pyproject.toml - Add [tool.dbterd] section to your existing Python project config

Learn more in the Configuration Files Guide.

💡 Examples

CLI Examples

# 📊 Select all models in dbt_resto
dbterd run -ad samples/dbtresto

# 🎯 Select multiple dbt resources (models + sources)
dbterd run -ad samples/dbtresto -rt model -rt source

# 🔍 Select models excluding staging
dbterd run -ad samples/dbtresto -s model.dbt_resto -ns model.dbt_resto.staging

# 📋 Select by schema name
dbterd run -ad samples/dbtresto -s schema:mart -ns model.dbt_resto.staging

# 🏷️ Select by full schema name
dbterd run -ad samples/dbtresto -s schema:dbt.mart -ns model.dbt_resto.staging

# 🌟 Other sample projects
dbterd run -ad samples/fivetranlog -rt model -rt source
dbterd run -ad samples/facebookad -rt model -rt source
dbterd run -ad samples/shopify -s wildcard:*shopify.shopify__*

# 🔗 Custom relationship detection
dbterd run -ad samples/dbt-constraints -a "test_relationship:(name:foreign_key|c_from:fk_column_name|c_to:pk_column_name)"

# 💻 Your local project
dbterd run -ad samples/local -rt model -rt source

Python API Examples

Generate Complete ERD

from dbterd.api import DbtErd

# Generate DBML format
erd = DbtErd().get_erd()
print("ERD (DBML):", erd)

# Generate Mermaid format
erd = DbtErd(target="mermaid").get_erd()
print("ERD (Mermaid):", erd)

# Generate canonical JSON payload (nodes/edges/metadata)
erd = DbtErd(target="json").get_erd()
print("ERD (JSON):", erd)

Generate Single Model ERD

from dbterd.api import DbtErd

# Get ERD for specific model
dim_prize_erd = DbtErd(target="mermaid").get_model_erd(
    node_unique_id="model.dbt_resto.dim_prize"
)
print("ERD of dim_prize (Mermaid):", dim_prize_erd)

Sample Output:

erDiagram
  "MODEL.DBT_RESTO.DIM_PRIZE" {
    varchar prize_key
    nvarchar prize_name
    int prize_order
  }
  "MODEL.DBT_RESTO.FACT_RESULT" {
    varchar fact_result_key
    varchar box_key
    varchar prize_key
    date date_key
    int no_of_won
    float prize_value
    float prize_paid
    int is_prize_taken
  }
  "MODEL.DBT_RESTO.FACT_RESULT" }|--|| "MODEL.DBT_RESTO.DIM_PRIZE": prize_key

🤝 Contributing

We welcome contributions! Whether you've found a bug, dreamed up a feature, or just want to fix a typo — you're very welcome here.

Ways to contribute: 🐛 Report bugs | 💡 Suggest features | 📝 Improve documentation | 🔧 Submit pull requests

See our Contributing Guide for detailed information.

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👥 Contributors

A huge thanks to our amazing contributors — the people who turned "wouldn't it be nice if..." into actual working code. 🙏

📧 Support

Need help? We're here for you! Check 📖 Documentation, 🐛 Report Issues and 💬 Discussions