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:

🧪 Test Relationships (default method)
🏛️ Semantic Entities (use -a semantic option)
📜 Model Contract Constraints (use -a model_contract option, requires dbt 1.9+ / manifest v12+)

For detailed configuration options, see our CLI References.

🎨 Supported Output Formats¶

Format	Description	Use Case
DBML	Database Markup Language	Interactive web diagrams
Mermaid	Markdown-friendly diagrams	Documentation, GitHub
PlantUML	Text-based UML	Technical documentation
GraphViz	DOT graph description	Complex relationship visualization
D2	Modern diagram scripting	Beautiful, customizable diagrams
DrawDB	Web-based database designer	Interactive database design
JSON	Canonical, schema-validated ERD payload	Tooling & integrations (VS Code extension, custom apps)

🌟 Ecosystem¶

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

Project	What it does
`dbdocs`	An alternative dbt docs site — catalog, ERD, and column-level lineage baked into a single self-contained HTML file.
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.

🚀 Installation¶

Requires Python 3.10+

dbterd 1.25 was the last release to support Python 3.9; support was dropped since 1.26. Python 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 — so upgrading your interpreter is the way forward (it's worth it). Still stuck on 3.9? Pin dbterd==1.25.*.

pip install dbterd --upgrade Successfully installed dbterd restart ↻

Verify Installation:

dbterd --version

For dbt-core users

It's highly recommended to keep artifact-parser updated to the latest version to support newer dbt-core versions and their manifest/catalog json schemas:

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

⚙️ Configuration Files¶

Tired of typing the same CLI arguments repeatedly? 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¶

🖱️ Click to explore 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 a 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 a 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

🎯 Try the Quick Demo with DBML format!

🤝 Contributing¶

We welcome contributions! 🎉

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

See our Contributing Guide for detailed information.

Show your support: - ⭐ Star this repository - 📢 Share on social media - ✍️ Write a blog post - ☕ Buy me a coffee

👥 Contributors¶

A huge thanks to our amazing contributors! 🙏

**Made with ❤️ by the dbterd community**