dg-sqlmesh 1.9.1

Seamless integration between Dagster and SQLMesh for modern data engineering workflows

SQLMesh-Dagster Integration

This module provides a complete integration between SQLMesh and Dagster, allowing SQLMesh models to be materialized as Dagster assets with support for audits, metadata, and adaptive scheduling.

Features

🎯 SQLMesh Model to Dagster Asset Conversion

• Individual asset control : Each SQLMesh model becomes a separate Dagster asset with granular success/failure control
• Automatic materialization : SQLMesh models are automatically converted to Dagster assets
• External assets support : SQLMesh sources (external assets) are mapped to Dagster AssetKeys
• 🆕 Jinja2 template mapping : Easy external asset mapping with external_asset_mapping parameter
• Automatic dependencies : Dependencies between models are preserved in Dagster
• Partitioning : Support for partitioned SQLMesh models (managed by SQLMesh, no integration with Dagster partitions - no Dagster → SQLMesh backfill)

📊 SQLMesh Metadata Integration to Dagster

• Complete metadata : Cron, tags, kind, dialect, query, partitioned_by, clustered_by
• Code versioning : Uses SQLMesh data_hash for Dagster versioning
• Column descriptions : Table metadata with descriptions
• Customizable tags : SQLMesh tags mapping to Dagster

✅ SQLMesh Audits to Asset Checks Conversion

• Automatic audits : SQLMesh audits become Dagster AssetCheckSpec
• AssetCheckResult : Automatic emission of audit results with proper output handling
• Audit metadata : SQL query, arguments, dialect, blocking status
• Non-blocking : Dagster checks are non-blocking (SQLMesh handles blocking)
• Notifier-based capture : Audit failures captured via notifier service (no console)

⏰ Adaptive Scheduling

• Automatic analysis : Detection of the finest granularity from SQLMesh crons
• Adaptive schedule : Automatic creation of a Dagster schedule based on crons
• Intelligent execution : SQLMesh manages which models should be executed
• Monitoring : Detailed logs and granularity metadata

🔧 All-in-One Factory

• Simple configuration : Single function to configure everything
• Extensible translator : Customizable translator system
• Automatic validation : External dependencies validation
• Retry policy : Centralized retry policy configuration

SQLMesh Feature Coverage

SQLMesh Feature	Status	Dagster Integration	Notes
Model Types
FULL models	✅ Supported	Individual assets with full materialization	Complete rebuild on each run
INCREMENTAL models	✅ Supported	Individual assets with incremental logic	SQLMesh handles incremental logic, no Dagster partition binding
SEED models	✅ Supported	Individual assets for data loading	CSV/Parquet file loading
EXTERNAL models	✅ Supported	External asset mapping	Sources from other systems
VIEW models	✅ Supported	Individual assets as views	Virtual tables
TABLE models	✅ Supported	Individual assets as tables	Materialized tables
Model Properties
Cron scheduling	✅ Supported	Adaptive schedule creation	Automatic cron analysis
Tags	✅ Supported	Dagster tag mapping	dagster:property:value convention
Audits	✅ Supported	AssetCheckSpec conversion	Automatic audit to check mapping
Column descriptions	✅ Supported	Table metadata	Rich schema information
Partitioning	✅ Supported	Metadata extraction	SQLMesh-managed partitions
Grain definition	✅ Supported	Metadata extraction	Data granularity info
Execution Features
Plan validation	✅ Supported	Combined plan/run execution	Validation before materialization
Run execution	✅ Supported	Materialization orchestration	Single SQLMesh run per Dagster run
Environment management	❌ Not supported	External responsibility	CLI/CI-CD managed
Breaking changes	❌ Not supported	External responsibility	CLI/CI-CD managed
Advanced Features
Multi-dialect support	✅ Supported	Dialect metadata	PostgreSQL, DuckDB, etc.
Custom macros	✅ Supported	SQL execution	Full SQLMesh macro support
Model dependencies	✅ Supported	Dagster dependency graph	Automatic dependency resolution
Audit blocking	✅ Supported	Non-blocking checks	SQLMesh handles blocking logic
Future Features
Non-blocking audits	✅ Supported	AssetCheckResult with WARN severity	Recognizes _non_blocking suffix; SQLMesh manages blocking
Dagster → SQLMesh backfill	🔄 Planned	Partition integration	Direct Dagster partition control
Multi-environment orchestration	❌ Not supported	Dagster OSS does not support multi-tenancy	Use separate Dagster clusters per environment
Dagster-Specific Features
Dagster Component packaging	✅ Supported	Standalone Dagster component	Package as reusable Dagster (yaml DSL) component
Custom asset groups	✅ Supported	Automatic group assignment	Based on model path and tags
Asset selection & filtering	✅ Supported	Selective materialization	Materialize specific models or groups
Dagster UI integration	✅ Supported	Individual asset visibility	Each model visible as separate asset in UI
Asset check results	✅ Supported	Audit results as AssetCheckResult	SQLMesh audits converted to Dagster checks
Custom translators	✅ Supported	Extensible translator system	Custom mapping for external assets and metadata
Shared execution optimization	✅ Supported	Single SQLMesh run per Dagster run	SQLMeshResultsResource for shared state
Adaptive scheduling	✅ Supported	Automatic schedule creation	Based on SQLMesh cron analysis

Basic Usage

Simple Factory (Recommended)

from dagster import RetryPolicy, AssetKey, Backoff
from dg_sqlmesh import sqlmesh_definitions_factory

# All-in-one factory with external asset mapping!
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    gateway="postgres",
    external_asset_mapping="target/main/{node.name}",  # 🆕 NEW: Jinja2 template for external assets
    concurrency_limit=1,
    group_name="sqlmesh",
    op_tags={"team": "data", "env": "prod"},
    retry_policy=RetryPolicy(max_retries=1, delay=30.0, backoff=Backoff.EXPONENTIAL),
    enable_schedule=True,  # Enable adaptive scheduling
)

Advanced Configuration with Custom Translator

from dagster import RetryPolicy, AssetKey, Backoff
from dg_sqlmesh import sqlmesh_definitions_factory
from dg_sqlmesh import SQLMeshTranslator

class SlingToSqlmeshTranslator(SQLMeshTranslator):
    def get_external_asset_key(self, external_fqn: str) -> AssetKey:
        """
        Custom mapping for external assets.
        SQLMesh: 'jaffle_db.main.raw_source_customers' → Sling: ['target', 'main', 'raw_source_customers']
        """
        parts = external_fqn.replace('"', '').split('.')
        if len(parts) >= 3:
            catalog, schema, table = parts[0], parts[1], parts[2]
            return AssetKey(['target', 'main', table])
        return AssetKey(['external'] + parts[1:])

# All-in-one factory with custom translator (takes priority over external_asset_mapping)
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    gateway="postgres",
    translator=SlingToSqlmeshTranslator(),  # Custom translator takes priority
    external_asset_mapping="target/main/{node.name}",  # Ignored when translator is provided
    concurrency_limit=1,
    group_name="sqlmesh",
    op_tags={"team": "data", "env": "prod"},
    retry_policy=RetryPolicy(max_retries=1, delay=30.0, backoff=Backoff.EXPONENTIAL),
    enable_schedule=True,  # Enable adaptive scheduling
)

Advanced Configuration

from dagster import Definitions, RetryPolicy
from dg_sqlmesh import sqlmesh_assets_factory, sqlmesh_adaptive_schedule_factory
from dg_sqlmesh import SQLMeshResource
from dg_sqlmesh import SQLMeshTranslator

# SQLMesh resource configuration
sqlmesh_resource = SQLMeshResource(
    project_dir="sqlmesh_project",
    gateway="postgres",
    translator=SlingToSqlmeshTranslator(),
    concurrency_limit=1,
)

# SQLMesh assets configuration
sqlmesh_assets = sqlmesh_assets_factory(
    sqlmesh_resource=sqlmesh_resource,
    group_name="sqlmesh",
    op_tags={"team": "data", "env": "prod"},
    retry_policy=RetryPolicy(max_retries=1, delay=30.0, backoff=Backoff.EXPONENTIAL),
)

# Adaptive schedule and job created automatically
sqlmesh_adaptive_schedule, sqlmesh_job, _ = sqlmesh_adaptive_schedule_factory(
    sqlmesh_resource=sqlmesh_resource
)

defs = Definitions(
    assets=[sqlmesh_assets],
    jobs=[sqlmesh_job],
    schedules=[sqlmesh_adaptive_schedule],
    resources={
        "sqlmesh": sqlmesh_resource,
    },
)

External Asset Mapping

🆕 NEW: Jinja2 Template Mapping

The external_asset_mapping parameter allows you to easily map external SQLMesh sources (like Sling objects) to Dagster asset keys using Jinja2 templates:

from dg_sqlmesh import sqlmesh_definitions_factory

# Simple mapping: external sources → target/main/{table_name}
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    external_asset_mapping="target/main/{node.name}",
    # ...
)

# Advanced mapping with database and schema
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    external_asset_mapping="{node.database}/{node.schema}/{node.name}",
    # ...
)

# Custom prefix mapping
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    external_asset_mapping="sling/{node.name}",
    # ...
)

Available Template Variables

The following variables are available in your Jinja2 template:

• {node.database} : Database name (e.g., "jaffle_db")
• {node.schema} : Schema name (e.g., "main")
• {node.name} : Table name (e.g., "raw_source_customers")
• {node.fqn} : Full qualified name (e.g., "jaffle_db.main.raw_source_customers")

Examples

# Map to dbt-style naming
external_asset_mapping="target/main/{node.name}"
# Result: "jaffle_db.main.raw_source_customers" → ["target", "main", "raw_source_customers"]

# Map to database/schema/table structure
external_asset_mapping="{node.database}/{node.schema}/{node.name}"
# Result: "jaffle_db.main.raw_source_customers" → ["jaffle_db", "main", "raw_source_customers"]

# Map to custom prefix
external_asset_mapping="sling/{node.name}"
# Result: "jaffle_db.main.raw_source_customers" → ["sling", "raw_source_customers"]

# Map to simplified structure
external_asset_mapping="{node.name}"
# Result: "jaffle_db.main.raw_source_customers" → ["raw_source_customers"]

Conflict Resolution

When both translator and external_asset_mapping are provided, the custom translator takes priority:

# Custom translator takes priority
defs = sqlmesh_definitions_factory(
    project_dir="sqlmesh_project",
    translator=MyCustomTranslator(),  # ✅ Used
    external_asset_mapping="target/main/{node.name}",  # ❌ Ignored
    # ...
)

A warning will be issued when both are provided to clarify the behavior.

Custom Translator

To map external assets (SQLMesh sources) to your Dagster conventions, you can create a custom translator:

from dg_sqlmesh import SQLMeshTranslator
import dagster as dg

class MyCustomTranslator(SQLMeshTranslator):
    def get_external_asset_key(self, external_fqn: str) -> dg.AssetKey:
        """
        Custom mapping for external assets.
        Example: 'jaffle_db.main.raw_source_customers' → ['target', 'main', 'raw_source_customers']
        """
        parts = external_fqn.replace('"', '').split('.')
        # We ignore the catalog (jaffle_db), we take the rest
        return dg.AssetKey(['target'] + parts[1:])

    def get_group_name(self, context, model) -> str:
        """
        Custom mapping for groups.
        """
        model_name = getattr(model, "view_name", "")
        if model_name.startswith("stg_"):
            return "staging"
        elif model_name.startswith("mart_"):
            return "marts"
        return super().get_group_name(context, model)

Translator Methods

The SQLMeshTranslator exposes several methods you can override:

get_external_asset_key(external_fqn: str) -> AssetKey

Maps an external asset FQN to a Dagster AssetKey.

get_asset_key(model) -> AssetKey

Maps a SQLMesh model to a Dagster AssetKey.

get_group_name(context, model) -> str

Determines the group for a model.

get_tags(context, model) -> dict

Generates tags for a model.

get_metadata(model, keys: list[str]) -> dict

Extracts specified metadata from the model.

Asset Checks and Audits

Automatic Audit Conversion

SQLMesh audits are automatically converted to Dagster AssetCheckSpec:

# SQLMesh audit
MODEL (
    name customers,
    audits (
        not_null(column=id),
        unique_values(columns=[id, email])
    )
);

# Automatically becomes in Dagster
AssetCheckSpec(
    name="not_null",
    asset=AssetKey(["customers"]),
    blocking=False,  # SQLMesh handles blocking
    description="SQLMesh audit: not_null(column=id)"
)

AssetCheckResult Emission

During execution, audit results are emitted as AssetCheckResult:

AssetCheckResult(
    passed=True,
    asset_key=AssetKey(["customers"]),
    check_name="not_null",
    metadata={
        "sqlmesh_model_name": "customers",
        "audit_query": "SELECT COUNT(*) FROM customers WHERE id IS NULL",
        "audit_blocking": False,
        "audit_dialect": "postgres",
        "audit_args": {"column": "id"}
    }
)

Adaptive Scheduling

Automatic Cron Analysis

The system automatically analyzes all SQLMesh crons and determines the finest granularity:

# If you have models with different crons:
# - customers: @daily
# - orders: @hourly
# - events: */5 * * * * (every 5 minutes)

# The adaptive schedule will be: */5 * * * * (every 5 minutes)

Intelligent Execution

The schedule runs sqlmesh run on all models, but SQLMesh automatically manages which models should be executed:

# The schedule simply does:
sqlmesh_resource.context.run(
    execution_time=datetime.datetime.now(),
)

Architecture

Individual Asset Pattern

Each SQLMesh model becomes a separate Dagster asset that:

• Materializes independently : Each asset calls sqlmesh.materialize_assets_threaded() for its specific model
• Controls success/failure : Each asset can succeed or fail individually based on SQLMesh execution results
• Handles dependencies : Uses translator.get_model_deps_with_external() for proper dependency mapping
• Manages checks : Each asset handles its own audit results with AssetCheckResult outputs

Benefits of Individual Assets

• Granular control : Each asset can succeed or fail independently in the Dagster UI
• Clear visibility : See exactly which models are running, succeeded, or failed
• Individual retries : Failed assets can be retried without affecting others
• Better monitoring : Track performance and issues per model
• Flexible scheduling : Different assets can have different schedules if needed

SQLMeshResource

• Manages SQLMesh context and caching
• Implements strict singleton pattern
• Uses AnyIO for multithreading
• Accepts a custom translator

SQLMeshTranslator

• Maps SQLMesh concepts to Dagster
• Extensible via inheritance
• Handles external assets and dependencies

SQLMesh Metadata via Tags

You can pass metadata from SQLMesh models to Dagster assets using the tag convention dagster:property:value:

-- In your SQLMesh model
MODEL (
    name customers,
    tags ARRAY["dagster:group_name:sqlmesh_datamarts"],
    -- ... other model properties
);

Supported Properties

Currently supported Dagster properties via tags:

• dagster:group_name:value : Sets the Dagster asset group name
  • Example: "dagster:group_name:sqlmesh_datamarts"
  • Result: Asset will be in the "sqlmesh_datamarts" group

Tag Convention

The convention follows the pattern: dagster:property:value

• dagster : Prefix to indicate this is for Dagster
• property : The Dagster property to update on the asset
• value : The value to set for that property

Priority Order

When determining asset properties, the translator follows this priority:

1. SQLMesh tags : dagster:group_name:value (highest priority)
2. Factory parameter : group_name="sqlmesh" in factory call
3. Default logic : Automatic group determination based on model path

sqlmesh_definitions_factory

• All-in-one factory for simple configuration
• Automatically creates: resource, assets, job, schedule
• Validates external dependencies
• Returns Definitions directly

SQLMeshEventCaptureConsole

• Custom SQLMesh console to capture events
• Captures audit results for AssetCheckResult
• Handles metadata serialization

Plan + Run Architecture

Individual Asset Materialization

Each Dagster asset materializes its specific SQLMesh model using:

1. Model Selection : get_models_to_materialize() selects the specific model for the asset
2. Materialization : sqlmesh.materialize_assets_threaded() executes the model
3. Result Handling : Console events determine success/failure and audit results

Implementation Details

# In each individual asset
def model_asset(context: AssetExecutionContext, sqlmesh: SQLMeshResource):
    # Materialize this specific model
    models_to_materialize = get_models_to_materialize(
        [current_asset_spec.key],
        sqlmesh.get_models,
        sqlmesh.translator,
    )

    # Execute materialization
    plan = sqlmesh.materialize_assets_threaded(models_to_materialize, context=context)

    # Check results via console events
    failed_models_events = sqlmesh._console.get_failed_models_events()
    evaluation_events = sqlmesh._console.get_evaluation_events()

    # Return MaterializeResult + AssetCheckResult for audits
    return MaterializeResult(...), *check_results

This approach provides granular control while maintaining all SQLMesh integration features.

Performance

• Shared execution: A single SQLMesh run is triggered per Dagster run. The first selected asset starts the SQLMesh materialization for all selected models; subsequent assets reuse the captured results via SQLMeshResultsResource to determine what was materialized or skipped.
• Strict singleton : Only one active SQLMesh instance
• Caching : Contexts, models and translators are cached
• Multithreading : Uses AnyIO to avoid Dagster blocking
• Lazy loading : Resources are loaded on demand
• Early validation : External dependencies validation before execution
• Optimized execution : SQLMesh automatically skips models that don't need materialization

Development Workflow

SQLMesh Development Philosophy

This module follows SQLMesh's philosophy of separation of concerns:

• Development : Use SQLMesh CLI for development and schema changes
• Production : Use SQLMesh CLI for promoting changes
• Orchestration : Use this Dagster module only for running models

Development Workflow

1. Local Development

# Develop your models locally
sqlmesh plan dev
sqlmesh apply dev

# Test your changes
sqlmesh run dev

2. Production Promotion

# Promote changes to production
sqlmesh plan prod # ->manual operation to validate the plan (apply it)

# Or use CI/CD pipeline

3. Dagster Orchestration

# Dagster takes over for production runs
# - Automatic scheduling via adaptive schedule
# - Manual runs via Dagster UI
# - Only executes: sqlmesh run prod

Module Responsibilities

What this module DOES:

• ✅ Orchestrates sqlmesh run commands
• ✅ Schedules model execution
• ✅ Monitors execution and audits
• ✅ Emits Dagster events and metadata

What this module DOES NOT:

• ❌ Plan changes (sqlmesh plan)
• ❌ Apply changes (sqlmesh apply)
• ❌ Handle breaking changes
• ❌ Manage environments

Breaking Changes Management

Breaking changes are handled outside this module:

• Development : sqlmesh plan dev + manual review
• Production : sqlmesh plan prod + CI/CD approval
• Orchestration : This module only runs approved models

Environment Separation

# Development (SQLMesh CLI)
sqlmesh plan dev
sqlmesh apply dev
sqlmesh run dev

# Production (Dagster module)
# Automatically runs: sqlmesh run prod
# Based on schedules and triggers

This separation ensures:

• ✅ Clear responsibilities : Development vs Orchestration
• ✅ Safe deployments : Breaking changes handled by SQLMesh CLI
• ✅ Reliable orchestration : Dagster only runs approved models
• ✅ CI/CD friendly : Standard SQLMesh workflow for deployments

Quick Testing Commands

For quick testing of individual models during development:

# Test a single model (useful for debugging)
uv run dg launch --assets "jaffle_db/sqlmesh_jaffle_platform/stg_tweets"

# Test multiple models
uv run dg launch --assets "jaffle_db/sqlmesh_jaffle_platform/stg_customers,jaffle_db/sqlmesh_jaffle_platform/stg_orders"

# Test ALL SQLMesh models at once
uv run dg launch --assets "jaffle_db/sqlmesh_jaffle_platform/seed_model,jaffle_db/sqlmesh_jaffle_platform/stg_customers,jaffle_db/sqlmesh_jaffle_platform/stg_order_items,jaffle_db/sqlmesh_jaffle_platform/stg_orders,jaffle_db/sqlmesh_jaffle_platform/stg_products,jaffle_db/sqlmesh_jaffle_platform/stg_stores,jaffle_db/sqlmesh_jaffle_platform/stg_supplies,jaffle_db/sqlmesh_jaffle_platform/stg_tweets,jaffle_db/sqlmesh_jaffle_platform/incremental_model,jaffle_db/sqlmesh_jaffle_platform/products,jaffle_db/sqlmesh_jaffle_platform/stores,jaffle_db/sqlmesh_jaffle_platform/customers,jaffle_db/sqlmesh_jaffle_platform/order_items,jaffle_db/sqlmesh_jaffle_platform/full_model,jaffle_db/sqlmesh_jaffle_platform/supplies,jaffle_db/sqlmesh_jaffle_platform/tweets,jaffle_db/sqlmesh_jaffle_platform/orders"

# Test with specific partition
uv run dg launch --assets "jaffle_db/sqlmesh_jaffle_platform/stg_tweets" --partition "2024-01-01"

These commands are particularly useful for:

• ✅ Testing skip behavior : Models with @daily cron will be skipped outside their schedule
• ✅ Debugging audit failures : Quick feedback on blocking vs non-blocking audits
• ✅ Verifying execution status : See the new sqlmesh_execution_status AssetCheck results

Dagster Component (YAML Configuration)

The module also provides a Dagster component for declarative YAML configuration:

Component Usage

# defs.yaml
type: dg_sqlmesh.SQLMeshProjectComponent

attributes:
  sqlmesh_config:
    project_path: "{{ project_root }}/sqlmesh_project"
    gateway: "postgres"
    environment: "prod"
  concurrency_jobs_limit: 1
  default_group_name: "sqlmesh"
  op_tags:
    team: "data"
    env: "prod"
  # schedule_name and enable_schedule are optional with defaults
  # schedule_name: "sqlmesh_adaptive_schedule"  # default value
  # enable_schedule: true  # default value (creates schedule but doesn't activate it)
  external_asset_mapping: "target/main/{node.name}"

Scaffolding

Create a new SQLMesh project with the component:

# Create a new SQLMesh project
dagster scaffold component dg_sqlmesh.SQLMeshProjectComponent --init

# Or scaffold with an existing project
dagster scaffold component dg_sqlmesh.SQLMeshProjectComponent --project-path path/to/your/sqlmesh_project

Component Features

• Declarative Configuration: Configure SQLMesh integration through YAML
• Automatic Asset Creation: SQLMesh models become Dagster assets automatically
• Audit Integration: SQLMesh audits become Dagster asset checks
• Adaptive Scheduling: Automatic schedule creation based on SQLMesh crons
• Scaffolding: Generate new SQLMesh projects with dagster scaffold

For more details, see the component documentation.

Installation

pip install dg-sqlmesh

Requirements

• Python 3.11+
• Dagster 1.11.4+
• SQLMesh 0.206.1+

Required Dagster instance configuration (mandatory)

To guarantee safe, single-run orchestration of SQLMesh from Dagster, you must configure your Dagster instance to enforce a singleton concurrency on the module’s exposed key and to use a queued run coordinator.

1. Enforce queued run coordinator

run_coordinator:
  module: dagster._core.run_coordinator.queued_run_coordinator
  class: QueuedRunCoordinator

2. Enforce singleton on the module’s concurrency key

Recommended (Dagster OSS 1.11+):

tag_concurrency_limits:
  - key: dagster/concurrency_key
    value: sqlmesh_jobs_exclusive
    limit: 1

Alternative (if using instance-level concurrency config):

concurrency:
  - concurrency_key: sqlmesh_jobs_exclusive
    limit: 1

Notes:

• The module tags jobs with dagster/concurrency_key=sqlmesh_jobs_exclusive. Do not change this key.
• Manual materializations will hard-fail with a non-retriable error if the instance is not using QueuedRunCoordinator.
• The adaptive schedule also validates the coordinator at tick time and will skip/stop runs if another run is already active.

Why this is required (SQLMesh guidance)

SQLMesh does not encourage running multiple SQLMesh commands in parallel against the same project/environment. This module enforces a singleton execution to align with that guidance and avoid:

• State corruption or overwrites (e.g., snapshot/store inconsistencies)
• Conflicting DDL and schema migrations
• Race conditions on plan/apply/promote/invalidate operations
• Inconsistent audits and backfills
• Resource contention between concurrent jobs and any SQLMesh janitor/background tasks

Limitations

• Shared execution model : A single sqlmesh run is triggered per Dagster run; assets reuse shared results via SQLMeshResultsResource
• No Dagster → SQLMesh backfill : Partitions managed only by SQLMesh itself (run a materialization to backfill)
• Breaking changes : Handled outside the module (SQLMesh CLI or CI/CD)
• Environment management : SQLMesh CLI or CI/CD
• External asset mapping : Only supports basic Jinja2 templates, complex conditionals may not work as expected
• Schedule activation : Schedules are created but not automatically activated (manual activation required)

Troubleshooting

Common Issues

External asset mapping errors

• Cause : Translator doesn't handle FQN format
• Solution : Check get_external_asset_key method

External asset mapping template errors

• Cause : Invalid Jinja2 template syntax or unsupported variables
• Solution : Use only supported variables: {node.database}, {node.schema}, {node.name}, {node.fqn}
• Example : "target/main/{node.name}" ✅ vs "target/main/{{ node.name }}" ❌

Performance issues

• Cause : Too many models loaded
• Solution : Use concurrency_limit and caching

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

License

Apache 2.0 License - see LICENSE file for details.