---

name: dagster-conventions description: Expert guidance for Dagster data orchestration including assets, resources, schedules, sensors, partitions, testing, and ETL patterns. Use when building or extending Dagster projects, writing assets, configuring automation, or integrating with dbt/dlt/Sling.

Dagster Development Expert

Core Philosophy

Think in Assets: Dagster is built around the asset abstraction—persistent objects like tables, files, or models that your pipeline produces. Assets provide:

• Clear Lineage: Explicit dependencies define data flow
• Better Observability: Track what data exists and how it was created
• Improved Testability: Assets are just Python functions that can be tested directly
• Declarative Pipelines: Focus on what to produce, not how to execute

Assets over Ops: For most data pipelines, prefer assets over ops. Use ops only when the asset abstraction doesn't fit (non-data workflows, complex execution patterns).

Environment Separation: Use resources and EnvVar to maintain separate configurations for dev, staging, and production without code changes.

---

Quick Reference

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Core Concepts

Asset: A persistent object (table, file, model) that your pipeline produces. Define with @dg.asset.

Resource: External services/tools (databases, APIs) shared across assets. Define with ConfigurableResource.

Job: A selection of assets to execute together. Create with dg.define_asset_job().

Schedule: Time-based automation for jobs. Create with dg.ScheduleDefinition.

Sensor: Event-driven automation that watches for changes. Define with @dg.sensor.

Partition: Logical divisions of data (by date, category). Define with PartitionsDefinition.

Definitions: The container for all Dagster objects in a code location.

Component: Reusable, declarative building blocks that generate Definitions from configuration (YAML). Use for standardized patterns.

Declarative Automation: Modern automation framework where you set conditions on assets rather than scheduling jobs.

---

Assets Quick Reference

Basic Asset

Copyimport dagster as dg

@dg.asset
def my_asset() -> None:
    """Asset description appears in the UI."""
    # Your computation logic here
    pass

Asset with Dependencies

Copy@dg.asset
def downstream_asset(upstream_asset) -> dict:
    """Depends on upstream_asset by naming it as a parameter."""
    return {"processed": upstream_asset}

Asset with Metadata

Copy@dg.asset(
    group_name="analytics",
    key_prefix=["warehouse", "staging"],
    description="Cleaned customer data",
    owners=["team:data-engineering", "alice@example.com"],
    tags={"priority": "high", "domain": "sales"},
    code_version="1.2.0",
)
def customers() -> None:
    pass

Best Practices:

• Naming: Use nouns describing what is produced (customers, daily_revenue), not verbs (load_customers)
• Tags: Primary mechanism for organization (use liberally)
• Owners: Specify team or individual owners for accountability
• code_version: Track when asset logic changes for lineage

---

Resources Quick Reference

Define a Resource

Copyfrom dagster import ConfigurableResource

class DatabaseResource(ConfigurableResource):
    connection_string: str

    def query(self, sql: str) -> list:
        # Implementation here
        pass

Use in Assets

Copy@dg.asset
def my_asset(database: DatabaseResource) -> None:
    results = database.query("SELECT * FROM table")

Register in Definitions

Copydg.Definitions(
    assets=[my_asset],
    resources={"database": DatabaseResource(connection_string="...")},
)

---

Automation Quick Reference

Schedule

Copyimport dagster as dg
from my_project.defs.jobs import my_job

my_schedule = dg.ScheduleDefinition(
    job=my_job,
    cron_schedule="0 0 * * *",  # Daily at midnight
)

Common Cron Patterns

---

Declarative Automation Quick Reference

Modern automation pattern: Set conditions on assets instead of scheduling jobs.

AutomationCondition Examples

Copyfrom dagster import AutomationCondition

# Update when upstream data changes
@dg.asset(
    automation_condition=AutomationCondition.on_missing()
)
def my_asset() -> None:
    pass

# Update daily at a specific time
@dg.asset(
    automation_condition=AutomationCondition.on_cron("0 9 * * *")
)
def daily_report() -> None:
    pass

# Combine conditions
@dg.asset(
    automation_condition=(
        AutomationCondition.on_missing()
        | AutomationCondition.on_cron("0 0 * * *")
    )
)
def flexible_asset() -> None:
    pass

Benefits over Schedules:

• More expressive condition logic
• Asset-native (no separate job definitions needed)
• Automatic dependency-aware execution
• Better for complex automation scenarios

When to Use:

• Asset-centric pipelines with complex update logic
• Condition-based triggers (data availability, freshness)
• Prefer over schedules for new projects

---

Sensors Quick Reference

Basic Sensor Pattern

Copy@dg.sensor(job=my_job)
def my_sensor(context: dg.SensorEvaluationContext):
    # 1. Read cursor (previous state)
    previous_state = json.loads(context.cursor) if context.cursor else {}
    current_state = {}
    runs_to_request = []

    # 2. Check for changes
    for item in get_items_to_check():
        current_state[item.id] = item.modified_at
        if item.id not in previous_state or previous_state[item.id] != item.modified_at:
            runs_to_request.append(dg.RunRequest(
                run_key=f"run_{item.id}_{item.modified_at}",
                run_config={...}
            ))

    # 3. Return result with updated cursor
    return dg.SensorResult(
        run_requests=runs_to_request,
        cursor=json.dumps(current_state)
    )

Key: Use cursors to track state between sensor evaluations.

---

Partitions Quick Reference

Time-Based Partition

Copyweekly_partition = dg.WeeklyPartitionsDefinition(start_date="2023-01-01")

@dg.asset(partitions_def=weekly_partition)
def weekly_data(context: dg.AssetExecutionContext) -> None:
    partition_key = context.partition_key  # e.g., "2023-01-01"
    # Process data for this partition

Static Partition

Copyregion_partition = dg.StaticPartitionsDefinition(["us-east", "us-west", "eu"])

@dg.asset(partitions_def=region_partition)
def regional_data(context: dg.AssetExecutionContext) -> None:
    region = context.partition_key

Partition Types

Best Practice: Limit partitions to 100,000 or fewer per asset for optimal UI performance.

---

Testing Quick Reference

Direct Function Testing

Copydef test_my_asset():
    result = my_asset()
    assert result == expected_value

Testing with Materialization

Copydef test_asset_graph():
    result = dg.materialize(
        assets=[asset_a, asset_b],
        resources={"database": mock_database},
    )
    assert result.success
    assert result.output_for_node("asset_b") == expected

Mocking Resources

Copyfrom unittest.mock import Mock

def test_with_mocked_resource():
    mocked_resource = Mock()
    mocked_resource.query.return_value = [{"id": 1}]

    result = dg.materialize(
        assets=[my_asset],
        resources={"database": mocked_resource},
    )
    assert result.success

Asset Checks

Copy@dg.asset_check(asset=my_asset)
def validate_non_empty(my_asset):
    return dg.AssetCheckResult(
        passed=len(my_asset) > 0,
        metadata={"row_count": len(my_asset)},
    )

---

dbt Integration

For dbt integration, prefer the component-based approach for standard dbt projects. Use Pythonic assets only when you need custom logic or fine-grained control.

Component-Based dbt (Recommended)

Use DbtProjectComponent with remote Git repository:

Copy# defs/transform/defs.yaml
type: dagster_dbt.DbtProjectComponent

attributes:
  project:
    repo_url: https://github.com/dagster-io/jaffle-platform.git
    repo_relative_path: jdbt
  dbt:
    target: dev

When to use:

• Standard dbt transformations
• Remote dbt project in Git repository
• Declarative configuration preferred
• Component reusability desired

For private repositories:

Copyattributes:
  project:
    repo_url: https://github.com/your-org/dbt-project.git
    repo_relative_path: dbt
    token: '{{ env.GIT_TOKEN }}'
  dbt:
    target: dev

Pythonic dbt Assets

For custom logic or local development:

Copyfrom dagster_dbt import DbtCliResource, dbt_assets
from pathlib import Path

dbt_project_dir = Path(__file__).parent / "dbt_project"

@dbt_assets(manifest=dbt_project_dir / "target" / "manifest.json")
def my_dbt_assets(context: dg.AssetExecutionContext, dbt: DbtCliResource):
    yield from dbt.cli(["build"], context=context).stream()

dg.Definitions(
    assets=[my_dbt_assets],
    resources={"dbt": DbtCliResource(project_dir=dbt_project_dir)},
)

When to use:

• Custom transformation logic needed
• Local development with frequent dbt code changes
• Fine-grained control over dbt execution

Full patterns: See Dagster dbt docs

---

When to Load References

Load references/assets.md when:

• Defining complex asset dependencies
• Adding metadata, groups, or key prefixes
• Working with asset factories
• Understanding asset materialization patterns

Load references/resources.md when:

• Creating custom ConfigurableResource classes
• Integrating with databases, APIs, or cloud services
• Understanding resource scoping and lifecycle

Load references/automation.md when:

• Creating schedules with complex cron patterns
• Building sensors with cursors and state management
• Implementing partitions and backfills
• Using declarative automation conditions
• Automating dbt or other integration runs

Load references/testing.md when:

• Writing unit tests for assets
• Mocking resources and dependencies
• Using dg.materialize() for integration tests
• Creating asset checks for data validation

Load references/etl-patterns.md when:

• Using dlt for embedded ETL
• Using Sling for database replication
• Loading data from files or APIs
• Integrating external ETL tools

Load references/project-structure.md when:

• Setting up a new Dagster project
• Configuring Definitions and code locations
• Using dg CLI for scaffolding
• Organizing large projects with Components

---

Project Structure

Recommended Layout

Copymy_project/
├── pyproject.toml
├── src/
│   └── my_project/
│       ├── definitions.py     # Main Definitions
│       └── defs/
│           ├── assets/
│           │   ├── __init__.py
│           │   └── my_assets.py
│           ├── jobs.py
│           ├── schedules.py
│           ├── sensors.py
│           └── resources.py
└── tests/
    └── test_assets.py

Definitions Pattern (Modern)

Auto-Discovery (Simplest):

Copy# src/my_project/definitions.py
from dagster import Definitions
from dagster_dg import load_defs

# Automatically discovers all definitions in defs/ folder
defs = Definitions.merge(
    load_defs()
)

Combining Components with Pythonic Assets:

Copy# src/my_project/definitions.py
from dagster import Definitions
from dagster_dg import load_defs
from my_project.assets import custom_assets

# Load component definitions from defs/ folder
component_defs = load_defs()

# Define pythonic assets separately
pythonic_defs = Definitions(
    assets=custom_assets,
    resources={...}
)

# Merge them together
defs = Definitions.merge(component_defs, pythonic_defs)

Traditional (Explicit):

Copy# src/my_project/definitions.py
from dagster import Definitions
from my_project.defs import assets, jobs, schedules, resources

defs = Definitions(
    assets=assets,
    jobs=jobs,
    schedules=schedules,
    resources=resources,
)

Scaffolding with dg CLI

Copy# Create new project
uvx create-dagster my_project

# Scaffold new asset file
dg scaffold defs dagster.asset assets/new_asset.py

# Scaffold schedule
dg scaffold defs dagster.schedule schedules.py

# Scaffold sensor
dg scaffold defs dagster.sensor sensors.py

# Validate definitions
dg check defs

---

Common Patterns

Job Definition

Copytrip_update_job = dg.define_asset_job(
    name="trip_update_job",
    selection=["taxi_trips", "taxi_zones"],
)

Run Configuration

Copyfrom dagster import Config

class MyAssetConfig(Config):
    filename: str
    limit: int = 100

@dg.asset
def configurable_asset(config: MyAssetConfig) -> None:
    print(f"Processing {config.filename} with limit {config.limit}")

Asset Dependencies with External Sources

Copy@dg.asset(deps=["external_table"])
def derived_asset() -> None:
    """Depends on external_table which isn't managed by Dagster."""
    pass

---

Anti-Patterns to Avoid

---

CLI Quick Reference

dg CLI (Recommended for Modern Projects)

Copy# Development
dg dev                          # Start Dagster UI (port 3000)
dg check defs                   # Validate definitions load correctly
dg list defs                    # Show all loaded definitions
dg list components              # Show available components

# Scaffolding
dg scaffold defs dagster.asset assets/file.py
dg scaffold defs dagster.schedule schedules.py
dg scaffold defs dagster.sensor sensors.py
dg scaffold defs dagster.resources resources.py

# Execution
dg launch --assets my_asset     # Materialize specific asset
dg launch --assets "*"          # Materialize all assets

dagster CLI (Legacy/General Purpose)

Copy# Use for non-dg projects or advanced scenarios
dagster dev                     # Start Dagster UI
dagster job execute -j my_job   # Execute a job
dagster asset materialize -a my_asset  # Materialize an asset

Use dg CLI for projects created with create-dagster. It provides auto-discovery, scaffolding, and modern workflow support.

---

References

• Assets: references/assets.md - Detailed asset patterns
• Resources: references/resources.md - Resource configuration
• Automation: references/automation.md - Schedules, sensors, partitions
• Testing: references/testing.md - Testing patterns and asset checks
• ETL Patterns: references/etl-patterns.md - dlt, Sling, file/API ingestion
• Project Structure: references/project-structure.md - Definitions, Components
• Official Docs: https://docs.dagster.io
• API Reference: https://docs.dagster.io/api/dagster