Dagster Development Expert

When to Use This Skill vs. Others

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

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

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",
    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

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="...")},
)

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

Declarative Automation Quick Reference

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

AutomationCondition Examples

from 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

@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

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

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

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)},
    )

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:

# 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:

attributes:
  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:

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()

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

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)

Auto-Discovery (Simplest):

# 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:

# 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):

# 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

# 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

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

Anti-Patterns to Avoid

CLI Quick Reference

dg CLI (Recommended for Modern Projects)

# 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 asset1 asset2               # Multiple assets
dg launch --assets "*"                         # Materialize all assets
dg launch --assets "tag:priority=high"         # Assets by tag
dg launch --assets "group:sales_analytics"     # Assets by group
dg launch --assets "kind:dbt"                  # Assets by kind
dg launch --job my_job                         # Execute a job

# Partitions
dg launch --assets my_asset --partition 2024-01-15              # Single partition
dg launch --assets my_asset --partition-range "2024-01-01...2024-01-31"  # Backfill range

# Configuration
dg launch --assets my_asset --config-json '{"ops": {"my_asset": {"config": {"param": "value"}}}}'

# Environment Variables
uv run dg launch --assets my_asset             # Auto-loads .env with uv
set -a; source .env; set +a; dg launch --assets my_asset  # Manual .env loading

# See /dg:launch command for comprehensive launch documentation

dagster CLI (Legacy/General Purpose)

# 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 and launching guidance
• 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
• Launch Command: /dg:launch – Comprehensive asset launching documentation
• Official Docs: https://docs.dagster.io
• API Reference: https://docs.dagster.io/api/dagster