Use or Subclass Existing Dagster Component

Overview

This skill helps you discover and work with existing Dagster integration components from the 70+ available integrations. It guides you through finding the right component, using it directly, or subclassing it to add custom functionality.

Key distinction: This skill differentiates between configuration-file-based components (like dbt and Sling) that read from local files, and API-based components (like Fivetran, PowerBI, Looker) that call external services. Configuration-based components should be used directly without demo_mode, while API-based components benefit from demo_mode implementation.

The documentation for subclassing components can be found here: https://docs.dagster.io/guides/build/components/creating-new-components/subclassing-components

What This Skill Does

When invoked, this skill will:

•✅ Help discover available Dagster integrations using uv run dg docs integrations --json or browsing https://docs.dagster.io/integrations/libraries
•✅ Determine whether to use the component directly or create a subclass
•✅ If subclassing: Create a local component subclass that extends the integration component
•✅ Implement demo mode support by overriding the execute() method
•✅ Add custom templating scope if needed via get_additional_scope()
•✅ Create and configure component instance YAML with proper type references
•✅ Validate that the component loads correctly using uv run dg check defs
•✅ Verify assets are created using uv run dg list defs

Prerequisites

Before running this skill, ensure:

•uv is installed (check with uv --version)
•You're in a Dagster project directory with the dg CLI available
•You know which integration you want to use (e.g., dbt, Looker, PowerBI, Fivetran)

Important: When to Switch Skills

If you discover the integration package exists but has NO Component class:

•Run: uv run python -c "import dagster_<integration>; print([x for x in dir(dagster_<integration>) if 'Component' in x])"
•If result is [], the integration doesn't have a Component class
•STOP and use the create-custom-dagster-component skill instead

This skill is ONLY for integrations that have existing Component classes to subclass.

Skill Workflow

Step 1: Discover Available Integrations

Help the user find the right integration component:

•
Run discovery command:
bash
```
uv run dg docs integrations --json
```
•
Alternative: Browse https://docs.dagster.io/integrations/libraries for visual list
•
Common integrations include:
- •dagster_dbt.DbtProjectComponent - dbt projects
- •dagster_fivetran.FivetranComponent - Fivetran syncs
- •dagster_sling.SlingReplicationCollectionComponent - Sling replications
- •dagster_powerbi.PowerBIWorkspaceComponent - PowerBI workspaces
- •dagster_looker.LookerComponent - Looker instances
- •dagster_airbyte.AirbyteComponent - Airbyte connections
- •dagster_databricks.DatabricksComponent - Databricks workflows
- •dagster_snowflake.SnowflakeComponent - Snowflake resources

Step 2: Determine Component Type and Use Case

IMPORTANT: First determine if this is a configuration-file-based or API-based component:

Configuration-File-Based Components

These components read from local configuration files and do NOT require external API credentials:

•DbtProjectComponent - Reads from dbt project files (dbt_project.yml, SQL/Python models)
•SlingReplicationCollectionComponent - Reads from replication YAML files
•Components that primarily process local files

For configuration-file-based components:

•✅ DO use the component directly or subclass for custom behavior
•❌ DO NOT implement demo_mode - just load the project files
•The component naturally works with local files without external dependencies

API-Based Components

These components call out to external services and require API credentials:

•FivetranComponent - Calls Fivetran API
•PowerBIWorkspaceComponent - Calls PowerBI API
•LookerComponent - Calls Looker API
•AirbyteComponent - Calls Airbyte API
•CensusComponent - Calls Census API

For API-based components:

•✅ DO implement demo_mode to mock external API calls
•✅ DO provide dummy credentials in YAML (ignored when demo_mode is true)
•This allows the component to work locally without external connections

Determine Your Use Case

Based on the component type, choose:

•Use directly (Configuration-based) - Load the component with local configuration files
•Use directly (API-based) - Use the component with real API credentials
•Subclass for demo mode (API-based only) - Add demo_mode to mock external API calls
•Subclass for other customization - Add other custom behavior beyond demo mode

If using directly, skip to Step 7 to create the component instance YAML.

Step 3: Install Integration Package

Install the required Dagster integration package:

bash

uv add dagster-<integration-name>

Examples:

•uv add dagster-dbt
•uv add dagster-sling
•uv add dagster-powerbi

Step 4: Scaffold Component Instance

Use dg scaffold defs to create the component instance directory:

bash

uv run dg scaffold defs <package>.<ComponentClass> <instance_name>

Example:

bash

uv run dg scaffold defs dagster_sling.SlingReplicationCollectionComponent my_sling_sync

This creates a directory structure like:

code

defs/
  my_sling_sync/
    defs.yaml
    # Other config files as needed

Step 5: Create Component Subclass

Create a component.py file in the component instance directory.

IMPORTANT: Only add demo_mode for API-based components!

•Configuration-file-based components (dbt, Sling) read from local files and don't need demo_mode
•API-based components (Fivetran, PowerBI, Looker) call external APIs and should use demo_mode

When extending integration components, you must understand whether they use dataclass or Pydantic BaseModel patterns, as this determines how to add custom fields like demo_mode.

Identifying Component Type

First, check the parent component's implementation:

python

# Check if it's a dataclass
uv run python -c "from <package> import <Component>; import dataclasses; print(dataclasses.is_dataclass(<Component>))"

Most Dagster integration components (like SlingReplicationCollectionComponent, DbtProjectComponent, FivetranComponent) use dataclass with the Resolvable interface.

Pattern for Dataclass-Based Components (Recommended)

This is the most common pattern for Dagster integration components:

python

from dataclasses import dataclass
import dagster as dg
from <integration_package> import <BaseComponentClass>

@dataclass
class Custom<ComponentName>(BaseComponentClass):
    """Customized component with demo mode support."""

    # New field - will automatically appear in YAML schema via Resolvable
    demo_mode: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        """Build definitions, using demo mode if enabled.

        Note: The parent class fields (like API credentials) are still set from YAML,
        but when demo_mode is True, we bypass the parent's build_defs() method
        and return mocked assets instead, so those credentials are never used.
        """
        if self.demo_mode:
            # Return mock assets for demo mode - parent credentials are ignored
            return self._build_demo_defs(context)
        else:
            # Use real integration with actual credentials from parent fields
            return super().build_defs(context)

    def _build_demo_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        """Build demo mode definitions with mocked assets."""
        @dg.asset(
            key=dg.AssetKey(["mock_asset"]),
            kinds={"integration_name"},  # IMPORTANT: Add the integration kind
        )
        def mock_asset(context: dg.AssetExecutionContext):
            context.log.info("Demo mode: simulating asset execution")
            return {"status": "demo_mode"}

        return dg.Definitions(assets=[mock_asset])

Key points for dataclass components:

•Use @dataclass decorator on your subclass
•Add fields with type annotations - they automatically become YAML schema fields
•Use field(default_factory=...) for mutable defaults (lists, dicts)
•The Resolvable interface (inherited from parent) handles YAML schema generation
•All fields with defaults must come after fields without defaults

Example with multiple custom fields:

python

from dataclasses import dataclass, field
import dagster as dg
from dagster_sling import SlingReplicationCollectionComponent

@dataclass
class CustomSlingComponent(SlingReplicationCollectionComponent):
    """Extended Sling component with additional configuration."""

    # New fields - all will appear in YAML schema
    demo_mode: bool = False
    enable_notifications: bool = False
    notification_channel: str = "slack"
    custom_tags: list[str] = field(default_factory=list)

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        if self.demo_mode:
            return self._build_demo_defs(context)
        return super().build_defs(context)

Pattern for Pydantic BaseModel Components (Rare)

Some components may use Pydantic BaseModel. In these cases, inherit from both the parent component and dg.Model:

python

import dagster as dg
from <integration_package> import <BaseComponentClass>

class Custom<ComponentName>(BaseComponentClass, dg.Model):
    """Customized component with demo mode support."""

    # New field - will appear in YAML schema
    demo_mode: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        if self.demo_mode:
            return self._build_demo_defs(context)
        return super().build_defs(context)

Pattern for Legacy execute()-based Components

For older components that override execute() instead of build_defs():

python

from dataclasses import dataclass
from dagster import AssetExecutionContext
from <integration_package> import <BaseComponentClass>
from collections.abc import Iterator
from typing import Any

@dataclass
class Custom<ComponentName>(BaseComponentClass):
    """Customized component with demo mode support."""

    demo_mode: bool = False

    def execute(
        self,
        context: AssetExecutionContext,
        **kwargs: Any,
    ) -> Iterator:
        """Custom execution logic with demo mode support."""
        if self.demo_mode:
            context.log.info("Running in demo mode with mocked data")
            yield from self._execute_demo_mode(context, **kwargs)
        else:
            context.log.info("Running with real integration")
            yield from super().execute(context, **kwargs)

    def _execute_demo_mode(
        self,
        context: AssetExecutionContext,
        **kwargs: Any,
    ) -> Iterator:
        """Demo mode implementation."""
        from dagster import Output
        context.log.info("Simulating integration execution locally")
        yield Output(value=None, output_name="result")

Key customization points:

•Override build_defs() or execute() - Check demo_mode and return mock data
•Use dataclass fields - They automatically become YAML schema fields via Resolvable
•Provide default values - All custom fields should have defaults
•Add custom templating - Override get_additional_scope() for YAML templating

Example with custom templating:

python

from dataclasses import dataclass
from collections.abc import Mapping
from typing import Any
import dagster as dg
from <integration_package> import <BaseComponentClass>

@dataclass
class Custom<ComponentName>(BaseComponentClass):
    demo_mode: bool = False

    @classmethod
    def get_additional_scope(cls) -> Mapping[str, Any]:
        """Add custom YAML templating functions."""
        def _custom_cron(cron_schedule: str) -> dg.AutomationCondition:
            return (
                dg.AutomationCondition.on_cron(cron_schedule)
                & ~dg.AutomationCondition.in_progress()
            )
        return {"custom_cron": _custom_cron}

Step 5.5: Align Asset Keys for Downstream Components (CRITICAL for Multi-Component Pipelines)

When to do this: If other Dagster components in your pipeline will depend on assets from this component, override get_asset_spec() to generate asset keys that match downstream expectations.

This applies when:

•dbt models will consume these assets
•Custom Dagster assets will reference these assets in their deps
•Another integration component (Hightouch, Census, BI tools) expects specific asset key patterns
•You want to simplify multi-level nested keys for easier downstream consumption

Why This Matters

By default, integration components generate asset keys in their own structure. For example:

•Fivetran: ["fivetran", "raw", "customers"]
•Sling: ["sling", "replications", "sync_name", "table"]
•dbt: ["analytics", "marts", "customer_360"]

The problem: Downstream components may expect different key structures, leading to broken dependencies or requiring per-asset configuration with meta.dagster.asset_key.

The solution: Override get_asset_spec() in the upstream component to generate keys that downstream components naturally reference.

Pattern: Override get_asset_spec()

python

def get_asset_spec(self, props) -> dg.AssetSpec:
    """Override to generate asset keys matching downstream component expectations.

    This eliminates the need for meta.dagster.asset_key configuration in downstream
    components by aligning keys at the source.
    """
    base_spec = super().get_asset_spec(props)
    original_key = base_spec.key.path

    # Customize key structure for your pipeline
    # Example: Flatten nested keys for easier consumption
    custom_key = dg.AssetKey([...])  # Your key transformation logic

    return base_spec.replace_attributes(key=custom_key)

Example 1: Fivetran → dbt Pipeline

Problem: Fivetran creates ["fivetran", "raw", "customers"], but dbt expects ["fivetran_raw", "customers"]

Solution:

python

from dagster_fivetran import FivetranAccountComponent
from dagster_fivetran.translator import FivetranConnectorTableProps
import dagster as dg

class CustomFivetranComponent(FivetranAccountComponent):
    def get_asset_spec(self, props: FivetranConnectorTableProps) -> dg.AssetSpec:
        """Flatten asset keys for dbt compatibility."""
        base_spec = super().get_asset_spec(props)
        original_key = base_spec.key.path

        # Flatten: ["fivetran", "raw", "table"] -> ["fivetran_raw", "table"]
        if len(original_key) >= 2:
            flattened_key = dg.AssetKey(["fivetran_raw", "_".join(original_key[1:])])
        else:
            flattened_key = dg.AssetKey(["fivetran_raw", original_key[-1]])

        return base_spec.replace_attributes(key=flattened_key)

Result: dbt sources work automatically without meta.dagster configuration:

yaml

# sources.yml - references work naturally now
sources:
  - name: fivetran_raw
    tables:
      - name: customers  # Matches ["fivetran_raw", "customers"]

Example 2: Sling → Custom Processing

Problem: Sling creates ["sling", "replications", "sync_name", "table"], but custom processors expect ["raw", "table"]

Solution:

python

from dagster_sling import SlingReplicationCollectionComponent
import dagster as dg

class CustomSlingComponent(SlingReplicationCollectionComponent):
    def get_asset_spec(self, props) -> dg.AssetSpec:
        """Simplify keys for downstream consumption."""
        base_spec = super().get_asset_spec(props)
        original_key = base_spec.key.path

        # Simplify: [..., "table"] -> ["raw", "table"]
        simplified_key = dg.AssetKey(["raw", original_key[-1]])

        return base_spec.replace_attributes(key=simplified_key)

Result: Custom assets reference naturally:

python

@dg.asset(deps=[dg.AssetKey(["raw", "customers"])])
def process_customers(context): ...

Verify Asset Key Alignment

After implementing get_asset_spec(), verify dependencies are correct:

bash

# Check asset keys and dependencies
uv run dg list defs --json | uv run python -c "
import sys, json
assets = json.load(sys.stdin)['assets']
print('\\n'.join([f\"{a['key']}: deps={a.get('deps', [])}\" for a in assets]))
"

What to look for:

•Downstream assets should list upstream assets in their deps array
•No duplicate asset keys (e.g., both ["fivetran", "raw", "table"] and ["fivetran_raw", "table"])
•Asset keys match what downstream components expect

Key Principles

•Upstream defines, downstream consumes: The upstream component should generate keys that downstream expects
•Configure once, apply to all: One get_asset_spec() override applies to all assets the component creates
•Minimize downstream configuration: Eliminate the need for meta.dagster.asset_key in every downstream reference
•Document your key structure: Make it clear what asset key pattern your component produces

Step 6: Update Component YAML

Update defs.yaml to reference your custom subclass. Your new fields (like demo_mode) will be available in the YAML schema.

yaml

type: <project_name>.defs.<instance_name>.component.Custom<ComponentName>

attributes:
  # Your custom fields
  demo_mode: true
  # Parent class required fields
  <parent_field>: <value>

Example for Sling (Configuration-based - NO demo_mode):

Sling reads from local replication YAML files, so no demo_mode is needed:

yaml

type: dagster_sling.SlingReplicationCollectionComponent

attributes:
  replications:
    - path: replication.yaml

Example for dbt (Configuration-based - NO demo_mode):

dbt reads from local project files, so no demo_mode is needed:

yaml

type: dagster_dbt.DbtProjectComponent

attributes:
  project:
    project_dir: analytics_dbt

Example with dummy credentials for demo mode (API-based components only):

For API-based components, you MUST provide dummy values to satisfy parent class schema validation. These values remain in the YAML but are ignored when demo_mode is true:

Example for Looker:

yaml

type: my_project.defs.looker_dashboards.component.CustomLookerComponent

attributes:
  demo_mode: true
  # Dummy Looker credentials - required for schema validation but ignored in demo mode
  looker_resource:
    base_url: "https://demo.looker.com"
    client_id: "demo_client_id"
    client_secret: "demo_client_secret"

Example for PowerBI:

yaml

type: my_project.defs.powerbi_workspace.component.CustomPowerBIComponent

attributes:
  demo_mode: true
  # Dummy PowerBI credentials - required for schema validation but ignored in demo mode
  powerbi_resource:
    client_id: "demo_client_id"
    client_secret: "demo_client_secret"
    tenant_id: "demo_tenant_id"
  workspace_id: "demo_workspace_id"

Example for Fivetran:

yaml

type: my_project.defs.fivetran_sync.component.CustomFivetranComponent

attributes:
  demo_mode: true
  # Dummy Fivetran credentials - required for schema validation but ignored in demo mode
  fivetran_resource:
    api_key: "demo_api_key"
    api_secret: "demo_api_secret"
  connector_id: "demo_connector_id"

Important Notes:

•The type must be the fully qualified Python path to your custom component class
•ALWAYS provide dummy values for required parent fields - they MUST be present in the YAML for schema validation
•Keep dummy values UNCOMMENTED - they're required for the YAML to load properly
•Use realistic dummy values (URLs, IDs, etc.) that look like real credentials but aren't
•Your demo_mode implementation will ignore these values and use mocked data instead

To switch to real mode:

•Set demo_mode: false
•Replace dummy values in YAML with real credentials
•Redeploy

Step 7: Configure Component Attributes

Fill in the required attributes for the integration component. Consult the component's documentation:

•For dbt: https://docs.dagster.io/integrations/dbt
•For Fivetran: https://docs.dagster.io/integrations/fivetran
•For PowerBI: https://docs.dagster.io/integrations/powerbi
•For Sling: https://docs.dagster.io/integrations/sling
•For Looker: https://docs.dagster.io/integrations/looker

Common attributes include:

•Configuration file paths
•Resource credentials
•Asset specifications
•Scheduling/automation conditions

Step 8: Validate Setup and Asset Key Alignment

Run validation commands to ensure everything works:

bash

# Check that definitions load without errors
uv run dg check defs

# List all assets to verify they were created
uv run dg list defs

Verify that:

•✅ All expected assets from the integration are listed
•✅ The component instance is properly configured
•✅ No errors or warnings are shown
•✅ If using demo mode, verify it can run without external connections

CRITICAL: Verify Asset Key Alignment

Check that asset dependencies are correct by running:

bash

uv run dg list defs --json | uv run python -c "
import sys, json
data = json.load(sys.stdin)
assets = data.get('assets', [])
print('Asset Dependencies:\n')
for asset in assets:
    key = asset.get('key', 'unknown')
    deps = asset.get('deps', [])
    if deps:
        print(f'{key}')
        for dep in deps:
            print(f'  ← {dep}')
    else:
        print(f'{key} (no dependencies)')
    print()
"

What to verify:

•✅ Downstream assets list upstream assets in their deps array
•✅ No missing dependencies (especially critical for dbt models that should depend on source tables)
•✅ Asset keys are simple and descriptive (typically 2 levels: ["category", "name"])
•✅ Asset keys match what downstream consumers expect

Common Issues and Fixes:

•
dbt models not depending on upstream sources:
- •Problem: dbt staging models show no dependencies
- •Cause: SQL doesn't use {{ source('source_name', 'table') }}
- •Fix: Add select * from {{ source('source_name', 'table') }} references in SQL (even if just in a CTE that's not used for demo mode)
•
Asset keys don't match downstream expectations:
- •Problem: Fivetran creates ["fivetran", "connector", "schema", "table"] but dbt expects ["fivetran_raw", "table"]
- •Cause: Default component key structure is too nested
- •Fix: Override get_asset_spec() to flatten keys (see Step 5.5 for details)
•
Reverse ETL components can't find dbt models:
- •Problem: Hightouch/Census assets show missing dependencies
- •Cause: dbt model names don't match expected asset keys
- •Fix: Use simple dbt model names that match expected keys, or configure deps=[AssetKey(["model_name"])] explicitly

Step 9: Test Demo Mode (If Applicable)

If you implemented demo mode:

•Set demo_mode: true in the component YAML
•Run uv run dg check defs to verify it works locally
•Try materializing an asset to test the demo mode execution
•Document how to switch between demo and real modes

Testing commands:

bash

# Check definitions load
uv run dg check defs

# List available assets
uv run dg list defs

# Materialize a specific asset in demo mode
uv run dg materialize <asset_key>

Important: Always Add Kinds to Demo Assets

When creating demo mode assets, ALWAYS add the kinds parameter to properly categorize the asset by its integration type. This helps with:

•Filtering assets in the Dagster UI
•Understanding the technology stack at a glance
•Organizing assets by integration

Example with kinds:

python

@dg.asset(
    key=dg.AssetKey(["fivetran", "salesforce_sync"]),
    description="Demo Fivetran sync of Salesforce data",
    kinds={"fivetran"},  # ← REQUIRED: Add the integration kind
)
def fivetran_salesforce_sync(context: dg.AssetExecutionContext):
    context.log.info("Demo mode: Simulating Fivetran sync")
    return {"status": "success", "mode": "demo"}

Common integration kinds:

•kinds={"fivetran"} for Fivetran assets
•kinds={"dbt"} for dbt assets
•kinds={"census"} for Census assets
•kinds={"sling"} for Sling assets
•kinds={"powerbi"} for PowerBI assets
•kinds={"looker"} for Looker assets
•kinds={"airbyte"} for Airbyte assets

You can verify kinds are showing correctly by running:

bash

uv run dg list defs

The "Kinds" column should show the integration type for each asset.

Integration-Specific Guidance

dbt Components (Configuration-File-Based)

IMPORTANT: DbtProjectComponent is configuration-file-based and reads from local dbt project files. DO NOT implement demo_mode - instead, just load the dbt project directly.

Use the component directly:

yaml

# defs.yaml for dbt component
type: dagster_dbt.DbtProjectComponent

attributes:
  project:
    project_dir: my_dbt_project  # Path to local dbt project

Only subclass if you need custom behavior (NOT for demo mode):

python

from dataclasses import dataclass
from dagster_dbt import DbtProjectComponent
import dagster as dg

@dataclass
class CustomDbtComponent(DbtProjectComponent):
    """Custom dbt component with additional features."""

    # Add custom fields if needed (NOT demo_mode)
    enable_custom_logging: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        # Add custom behavior here
        defs = super().build_defs(context)

        if self.enable_custom_logging:
            # Custom logging logic
            pass

        return defs

The dbt component naturally works with local project files without requiring external API calls.

Sling Components (Configuration-File-Based)

IMPORTANT: SlingReplicationCollectionComponent is configuration-file-based and reads from local replication YAML files. DO NOT implement demo_mode - instead, just load the replication configuration.

Use the component directly:

yaml

# defs.yaml for Sling component
type: dagster_sling.SlingReplicationCollectionComponent

attributes:
  replications:
    - path: replication.yaml  # Path to local replication config

Only subclass if you need custom behavior (NOT for demo mode):

python

from dataclasses import dataclass
from dagster_sling import SlingReplicationCollectionComponent
import dagster as dg

@dataclass
class CustomSlingComponent(SlingReplicationCollectionComponent):
    """Custom Sling component with additional features."""

    # Add custom fields if needed (NOT demo_mode)
    enable_custom_validation: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        # Add custom behavior here
        defs = super().build_defs(context)

        if self.enable_custom_validation:
            # Custom validation logic
            pass

        return defs

The Sling component naturally works with local replication files without requiring external API calls.

Fivetran Components (API-Based)

Fivetran is API-based and requires credentials. Implement demo_mode to mock API calls:

python

from dataclasses import dataclass
from dagster_fivetran import FivetranComponent
import dagster as dg

@dataclass
class CustomFivetranComponent(FivetranComponent):
    """Fivetran component with demo mode support."""

    demo_mode: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        if self.demo_mode:
            return self._build_demo_defs(context)
        return super().build_defs(context)

    def _build_demo_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        @dg.asset(
            key=dg.AssetKey(["fivetran", "demo_sync"]),
            kinds={"fivetran"},
        )
        def fivetran_demo_sync(context: dg.AssetExecutionContext):
            context.log.info("Demo mode: Simulating Fivetran sync")
            return {"status": "demo_success", "records_synced": 1000}

        return dg.Definitions(assets=[fivetran_demo_sync])

PowerBI Components (API-Based)

PowerBI is API-based and requires credentials. Implement demo_mode to mock API calls:

python

from dataclasses import dataclass
from dagster_powerbi import PowerBIWorkspaceComponent
import dagster as dg

@dataclass
class CustomPowerBIComponent(PowerBIWorkspaceComponent):
    """PowerBI component with demo mode support."""

    demo_mode: bool = False

    def build_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        if self.demo_mode:
            return self._build_demo_defs(context)
        return super().build_defs(context)

    def _build_demo_defs(self, context: dg.ComponentLoadContext) -> dg.Definitions:
        @dg.asset(
            key=dg.AssetKey(["powerbi", "demo_dashboard"]),
            kinds={"powerbi"},
        )
        def powerbi_demo_dashboard(context: dg.AssetExecutionContext):
            context.log.info("Demo mode: Using cached PowerBI metadata")
            return {"dashboards": ["Sales Dashboard", "Marketing Dashboard"]}

        return dg.Definitions(assets=[powerbi_demo_dashboard])

Success Criteria

The component is complete when:

•✅ Integration component is discovered and selected
•✅ Component subclass is created (if needed) with proper inheritance
•✅ Demo mode is implemented in execute() method (if applicable)
•✅ Component YAML is properly configured with fully qualified type
•✅ All required attributes are specified
•✅ uv run dg check defs passes without errors
•✅ uv run dg list defs shows all expected assets from the integration
•✅ Demo mode works without external dependencies (if implemented)

Troubleshooting

Problem: Schema validation errors for missing required fields

Cause: The parent component requires certain fields, but you didn't provide them in YAML.

Solution: Provide dummy values for required fields. Keep them UNCOMMENTED - they're needed for schema validation but will be ignored in demo mode:

yaml

attributes:
  demo_mode: true
  # Dummy credentials - MUST be present and uncommented for schema validation
  looker_resource:
    base_url: "https://demo.looker.com"
    client_id: "demo_client_id"
    client_secret: "demo_client_secret"

Import Errors

If you see import errors, ensure:

•The integration package is installed: uv add dagster-<integration>
•The package name matches the import: from dagster_<name> import ...

Type Reference Errors

If the component can't be found:

•Verify the fully qualified type in defs.yaml matches your Python path
•Check that component.py is in the correct directory
•Ensure the class name matches exactly

Validation Failures

If dg check defs fails:

•Check that all required attributes are provided in YAML (use dummy values if needed)
•Verify resource configurations are valid
•Ensure demo mode doesn't require external connections
•Verify model_config = ConfigDict(extra="allow") is set in your Python class

Next Steps

After completion, inform the user:

•✅ The component has been created and validated
•📁 Location of the component files
•🔄 How to toggle demo mode (if applicable)
•📚 Links to integration-specific documentation
•🚀 How to customize further by overriding additional methods
•💡 How to add more component instances using the same pattern

Additional Resources

•Subclassing Components Guide: https://docs.dagster.io/guides/build/components/creating-new-components/subclassing-components
•Integration Libraries: https://docs.dagster.io/integrations/libraries
•Component API Reference: https://docs.dagster.io/llms-full.txt
•Discovery command: uv run dg docs integrations --json