Dagster Development Expert

Dagster Development Expert

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.

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Assets Quick Reference

Basic Asset

import dagster as dg

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

Asset with Dependencies

@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

@dg.asset(
    group_name="analytics",
    key_prefix=["warehouse", "staging"],
    description="Cleaned customer data",
)
def customers() -> None:
    pass

Naming: Use nouns describing what is produced (customers, daily_revenue), not verbs (load_customers).

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Resources Quick Reference

Define a Resource

from dagster import ConfigurableResource

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

Use in Assets

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

Register in Definitions

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

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Automation Quick Reference

Schedule

import 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

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Sensors Quick Reference

Basic Sensor Pattern

@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.

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Partitions Quick Reference

Time-Based Partition

weekly_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

region_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

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Testing Quick Reference

Direct Function Testing

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

Testing with Materialization

def 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

from 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

@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)},
    )

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dbt Integration

For dbt integration, use the minimal pattern below. For comprehensive dbt patterns, see the dbt-development skill.

Basic dbt Assets

from 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()

dbt Resource

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

Full patterns: See Dagster dbt docs

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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
• 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

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Project Structure

Recommended Layout

my_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)

# src/​my_project/​definitions.py
from pathlib import Path
from dagster import definitions, load_from_defs_folder

@definitions
def defs():
    return load_from_defs_folder(project_root=Path(__file__).parent.parent.parent)

Scaffolding with dg CLI

# 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

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Common Patterns

Job Definition

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

Run Configuration

from 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

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

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Anti-Patterns to Avoid

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CLI Quick Reference

# Development
dg dev                          # Start Dagster UI
dg check defs                   # Validate definitions

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

# Production
dagster job execute -j my_job   # Execute a job
dagster asset materialize -a my_asset  # Materialize an asset

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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