Working with Tags

Tags in dbt are powerful metadata labels that can be applied to various resources in your project. They enable flexible model selection, improved workflow management, and better project organization.

What Are Tags?

Tags are simple text labels you can assign to models, sources, snapshots, and other dbt resources. They help you organize, categorize, and select specific subsets of your project for execution or documentation.

By leveraging tags, you can:

• Group models logically – Categorize models based on refresh schedule, function, or ownership

• Control execution – Run or exclude specific sets of models

• Optimize CI/CD pipelines – Target models for incremental builds and tests

• Improve project maintainability – Standardize workflows across teams

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How to Apply Tags

Tags can be applied in two primary ways: directly in model files or in your project configuration file.

1. Defining Tags in a Model File

Tags can be assigned directly within SQL models using the config() function:

{{ config(
    tags=["finance", "daily_refresh"]
) }}

SELECT *
FROM {{ ref('stg_transactions') }}

This assigns both the "finance" and "daily_refresh" tags to this specific model.

2. Defining Tags in dbt_project.yml

Tags can also be applied at the project level, affecting entire folders or groups of models:

models:
  my_project:
    +tags: "core"  # Assigns a single tag

    staging:
      +tags: ["staging", "raw_data"]  # Multiple tags

    marts:
      +tags:
        - "mart"
        - "business_logic"

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Tag Inheritance

Models inside a folder inherit the parent folder's tags unless overridden. This creates a hierarchical tagging system that is easy to maintain.

Example:

models/
  staging/
    customers.sql   → Tags: ["staging", "raw_data"]
    orders.sql      → Tags: ["staging", "raw_data"]
  marts/
    dim_customer.sql → Tags: ["mart", "business_logic"]

Individual models can add to inherited tags:

-- models/staging/special_orders.sql
{{ config(
    tags=["critical"]  # Adds to inherited ["staging", "raw_data"]
) }}

SELECT * FROM {{ ref('stg_orders') }}

This model would have tags: ["staging", "raw_data", "critical"]

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Applying Tags to Different Resource Types

Tags can be applied to various dbt resource types:

Snapshots

snapshots:
  my_project:
    +tags: ["historical_data"]

Seeds

seeds:
  my_project:
    +tags: ["seed_data"]

Sources

sources:
  - name: external_source
    tags: ['external']

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Using Tags for Selection

Once tags are defined, you can use them with dbt commands to select specific resources.

Selection Examples

Command	Description
dbt run --select tag:daily_refresh	Run only models with the daily_refresh tag
dbt run --select tag:daily_refresh tag:critical	Run models with either daily_refresh OR critical tags
dbt run --select tag:daily_refresh --exclude tag:deprecated	Run models with daily_refresh but exclude those with deprecated tag
dbt run --select staging,tag:finance	Run all models tagged finance in the staging folder
dbt run --select tag:critical+	Run critical models and their downstream dependencies

Tag Selection Patterns

Selection Pattern	What It Selects	Example
tag:name	All resources with this tag	dbt run --select tag:nightly
tag:name1 tag:name2	Resources with either tag	dbt run --select tag:nightly tag:critical
tag:name+	Tagged resources and downstream dependencies	dbt run --select tag:base+
+tag:name	Tagged resources and upstream dependencies	dbt run --select +tag:reporting
--exclude tag:name	Everything except resources with this tag	dbt run --exclude tag:deprecated

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Best Practices for Using Tags

Use Consistent Naming Conventions

Standardized naming improves clarity and prevents confusion.

# Good
+tags: ["daily_refresh", "finance_data"]

# Avoid inconsistent casing or spacing
+tags: ["Daily", "Finance", "financial-data"]

Document Your Tagging Strategy

Clearly define tag meanings in your project's documentation.

# Tag Definitions
- `daily_refresh`: Models refreshed daily.
- `finance_data`: Contains financial-related tables.
- `pii`: Includes personally identifiable information.

Use Granular Tags

Avoid broad, generic tags. Instead, use precise labels for better control.

# Good
+tags: ["customer_metrics", "daily_refresh"]

# Too broad
+tags: ["metrics", "regular"]

Tag Models by Layer

Use tags to represent data modeling layers in your project.

models:
  staging:
    +tags: ["bronze_layer"]
  intermediate:
    +tags: ["silver_layer"]
  marts:
    +tags: ["gold_layer"]

---

Common Use Cases for Tags

Refresh Scheduling

Define tags based on refresh frequency for better execution control:

models:
  my_project:
    hourly:
      +tags: ["hourly_refresh"]
    daily:
      +tags: ["daily_refresh"]

Then in your orchestration tool, schedule different runs:

# Morning run for daily models
dbt run --select tag:daily_refresh

# Every hour for hourly models
dbt run --select tag:hourly_refresh

Data Classification

Differentiate datasets based on sensitivity or access level:

models:
  my_project:
    +tags: ["contains_pii"]
    public:
      +tags: ["public_data"]

This helps implement appropriate security controls and auditing.

Testing Strategy

Prioritize critical models in testing workflows:

models:
  my_project:
    critical:
      +tags: ["critical_path", "requires_alert"]

Run critical tests more frequently:

dbt test --select tag:critical_path

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Troubleshooting Tag Issues

If dbt isn't selecting resources correctly based on tags, consider these troubleshooting steps:

Tag Inheritance Issues

Verify parent folder configurations in dbt_project.yml:

# List all models with their tags
dbt ls -m "my_project.staging.*" --output path

Selection Syntax Errors

Ensure tag names match exactly (case-sensitive):

# Try with exact case
dbt run --select tag:daily_refresh  # Not tag:Daily_Refresh

Using dbt ls to Validate Tags

Use the dbt ls command to check which models have specific tags:

# List all models with the "finance" tag
dbt ls --select tag:finance

By effectively implementing a tagging strategy, you can organize your dbt project more efficiently, streamline your workflows, and gain better control over how your transformations are executed.