🏷️ the dbt DAG: How Model Dependencies Work in dbt

In data engineering, maintaining clarity about how data flows between models is essential. The dbt DAG (Directed Acyclic Graph) is one of dbt’s most powerful features, giving you an instant, visual understanding of model dependencies and build order.

Every time you use ref() in dbt, you are creating a connection between two models. These connections form a graph that dbt uses to determine which model depends on which — and in what order to build them.

This dependency graph is what we call the dbt DAG.

In this article, we will explore the dbt DAG from top to bottom — including:

• What it is and how it works
• Real-world use cases
• Three unique example programs
• A
• Memory tricks for interviews
• Why it’s important to master this concept

Let’s begin by understanding the concept clearly.

🧩 1. What Is the dbt DAG?

Definition

The dbt DAG (Directed Acyclic Graph) represents the order and dependencies among your dbt models, tests, snapshots, and sources.

• Directed: Each edge in the graph points in one direction (from upstream → downstream).
• Acyclic: There are no circular dependencies — you can’t have a model that ultimately depends on itself.
• Graph: The models and their relationships form nodes (models) and edges (connections).

Every time you define a model and reference another using {{ ref('model_name') }}, dbt automatically adds a link between those two models in the DAG.

Example

If orders.sql references customers.sql:

select
  o.order_id,
  c.customer_name
from {{ ref('orders') }} o
join {{ ref('customers') }} c on o.customer_id = c.id

Then the DAG has a link: raw_customers → orders

When you run dbt run, dbt reads this DAG and executes the models in dependency order.

⚙️ 2. How dbt DAG Works

1. Parsing Phase: dbt scans all .sql models and Jinja templates in your /models directory.

2. Graph Building: Each {{ ref('...') }} creates a directed edge between two nodes (models).

3. Topological Sorting: dbt uses a topological sort algorithm to decide the build sequence — ensuring dependencies build before dependents.

4. Execution Phase: dbt executes the models according to the sorted order (from source → intermediate → final).

5. Visualization: You can view your DAG using:

   • dbt Cloud: Click “Lineage” or “DAG View.”
   • dbt Docs: Run dbt docs generate and dbt docs serve.
   • The DAG appears as a graph connecting your models and sources visually.

🧠 3. Why Is It Called “Acyclic”?

Because dbt prevents loops. For example:

If model_A references model_B and model_B references model_A, dbt will throw a compilation error — you can’t have circular dependencies.

This acyclic structure ensures that build order is deterministic and avoids infinite loops during execution.

🔍 4. Key Benefits of the dbt DAG

💡 5. Visual Example of a dbt DAG

Let’s imagine three models:

raw_customers → stg_customers → dim_customers

Flow:

1. raw_customers is your raw source.
2. stg_customers cleans and standardizes it.
3. dim_customers aggregates or joins it for analytics.

The DAG visually represents this chain as arrows showing the dependency direction.

🧱 6. Example Program Set 1: Simple DAG

Model 1: raw_orders.sql

select * from analytics.raw_orders

Model 2: stg_orders.sql

{{ config(materialized='view') }}

select
    order_id,
    customer_id,
    order_date,
    total_amount
from {{ ref('raw_orders') }}
where total_amount > 0

Model 3: dim_orders.sql

{{ config(materialized='table') }}

select
    customer_id,
    count(order_id) as total_orders,
    sum(total_amount) as total_spent
from {{ ref('stg_orders') }}
group by 1

DAG Representation:

raw_orders → stg_orders → dim_orders

When you run:

dbt run --models dim_orders

dbt automatically builds raw_orders first, then stg_orders, and finally dim_orders.

🧮 7. Example Program Set 2: Intermediate Joins in DAG

Model 1: raw_products.sql

select * from analytics.raw_products

Model 2: raw_sales.sql

select * from analytics.raw_sales

Model 3: stg_sales_with_products.sql

{{ config(materialized='ephemeral') }}

select
    s.sale_id,
    s.product_id,
    p.product_name,
    s.quantity,
    s.price,
    s.quantity * s.price as total_value
from {{ ref('raw_sales') }} s
join {{ ref('raw_products') }} p on s.product_id = p.product_id

Model 4: agg_sales_summary.sql

{{ config(materialized='table') }}

select
    product_name,
    sum(total_value) as total_sales,
    avg(price) as avg_price
from {{ ref('stg_sales_with_products') }}
group by 1

DAG Representation:

raw_products
     │
     └──→ stg_sales_with_products → agg_sales_summary
raw_sales ──┘

🧾 8. Example Program Set 3: Multi-branch DAG

Model 1: raw_users.sql

select * from analytics.raw_users

Model 2: stg_users.sql

{{ config(materialized='view') }}
select id as user_id, lower(email) as email_clean from {{ ref('raw_users') }}

Model 3: user_activity.sql

{{ config(materialized='incremental') }}

select
    u.user_id,
    a.activity_date,
    a.page_views
from {{ ref('stg_users') }} u
join {{ ref('raw_activity') }} a on u.user_id = a.user_id

Model 4: user_summary.sql

{{ config(materialized='table') }}

select
    user_id,
    count(distinct activity_date) as active_days,
    sum(page_views) as total_views
from {{ ref('user_activity') }}
group by 1

DAG:

raw_users → stg_users → user_activity → user_summary

If another branch, say raw_orders → order_summary, exists, the DAG merges into a multi-branch graph, showing parallel dependencies.

🧭 9. : How dbt DAG Operates

          ┌──────────────────┐
          │   Raw Sources     │
          └───────┬──────────┘
                  │
          ▼
    ┌──────────────┐
    │  Staging     │ (Cleaning / Standardizing)
    └───────┬──────┘
            │
     ▼
  ┌──────────────┐
  │ Intermediate │ (Joins / Metrics)
  └───────┬──────┘
          │
   ▼
┌──────────────┐
│ Final Models │ (Aggregates / Business views)
└──────────────┘

Each arrow represents a ref() dependency. dbt traverses the graph top to bottom when running.

🧠 10. How to Remember This Concept (for Interviews & Exams)

Mnemonics

• “DAG = Direction Always Goes forward” → no cycles, only forward dependencies.
• “REF = Relationship Engine Function” — because ref() builds the graph.

Flashcard Questions

Interview Tips

1. Explain the flow visually — say “The DAG shows how raw → staging → intermediate → final layers are connected.”
2. Mention debugging benefits — “When a model fails, I can use the DAG to see all downstream impacts.”
3. Talk about performance — “Selective runs using dbt run --select +model_name depend on DAG understanding.”

📘 11. Why Learning dbt DAG Is Important

In modern data teams, lineage and transparency are essential for governance, auditing, and trust in analytics — and dbt DAG delivers that visually and programmatically.

⚠️ 12. Common Mistakes & Best Practices

Best Practices

1. Always use ref() and source() — never hardcode.
2. Keep model layers clear — raw, staging, marts, reports.
3. Visualize often using dbt docs serve.
4. Annotate models with descriptions and owners.
5. Use DAG to design incremental build strategies.

🧩 13. Real-Life Analogy

Think of dbt DAG as a recipe dependency chart:

• “Flour” (source data) must be ready before “Cake batter” (staging)
• “Cake batter” must exist before “Baked cake” (final table)

dbt DAG ensures you bake your models in the right order every time.

🧾 14. Sample Command-Based Insights

🧠 15. Key Takeaways

1. The dbt DAG is the backbone of dbt — it drives build order and lineage.
2. Each ref() builds a dependency edge.
3. It’s Directed (one-way flow) and Acyclic (no loops).
4. You can visualize the DAG via dbt docs or dbt Cloud.
5. Understanding the DAG helps with debugging, collaboration, and selective builds.
6. Always remember: ref = relationships, DAG = direction.

🧾 16. Summary

The dbt DAG (Directed Acyclic Graph) is more than just a visualization — it is the execution engine that controls the order and relationship of your dbt models.

By leveraging the DAG:

• You ensure clean data lineage.
• You can debug, optimize, and document your workflows.
• You make your data transformation pipeline predictable and scalable.

From small startups to enterprise warehouses, understanding and visualizing the dbt DAG is one of the most valuable skills for any data engineer or analytics developer.

🧭 Final Thoughts

If dbt models are the building blocks, then the DAG is the blueprint connecting them.

Learning the dbt DAG empowers you to:

• Write maintainable, modular SQL models.
• Understand lineage end-to-end.
• Run transformations efficiently.

So next time you build a model, remember — every ref() is a link in your DAG, and together they form the map of your entire analytics ecosystem.