0
# Workflow Orchestration
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The Databricks provider offers sophisticated workflow orchestration capabilities through Databricks Workflows, enabling you to create complex multi-task pipelines that run as coordinated jobs with dependency management, resource sharing, and advanced monitoring.
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## Core Components
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### DatabricksWorkflowTaskGroup
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Create coordinated workflows that execute as unified Databricks jobs with shared resources and dependencies.
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```python { .api }
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from airflow.providers.databricks.operators.databricks_workflow import DatabricksWorkflowTaskGroup
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class DatabricksWorkflowTaskGroup(TaskGroup):
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    def __init__(
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        self,
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        *,
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        group_id: str,
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        databricks_conn_id: str = "databricks_default",
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        existing_clusters: dict[str, str] | None = None,
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        extra_job_params: dict[str, Any] | None = None,
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        max_concurrent_runs: int = 1,
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        default_task_timeout_seconds: int | None = None,
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        default_task_retries: int = 0,
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        prefix: str = "",
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        suffix: str = "",
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        dag: DAG | None = None,
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        parent_group: TaskGroup | None = None,
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        **kwargs
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    ) -> None:
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        """
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        Create a workflow task group for coordinated Databricks job execution.
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        Args:
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            group_id: Unique identifier for the workflow group
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            databricks_conn_id: Airflow connection ID for Databricks
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            existing_clusters: Mapping of cluster keys to cluster IDs for reuse
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            extra_job_params: Additional parameters for the Databricks job
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            max_concurrent_runs: Maximum number of concurrent workflow runs
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            default_task_timeout_seconds: Default timeout for workflow tasks
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            default_task_retries: Default retry count for workflow tasks
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            prefix: Prefix for task names within the workflow
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            suffix: Suffix for task names within the workflow
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            dag: Parent DAG containing this workflow
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            parent_group: Parent task group if this is a nested workflow
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        """
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```
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### DatabricksTaskOperator
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Individual tasks within Databricks workflows with comprehensive configuration support.
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```python { .api }
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from airflow.providers.databricks.operators.databricks_workflow import DatabricksTaskOperator
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class DatabricksTaskOperator(BaseOperator):
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    def __init__(
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        self,
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        *,
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        task_config: dict[str, Any],
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        databricks_conn_id: str = "databricks_default",
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        timeout_seconds: int | None = None,
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        retries: int | None = None,
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        cluster_spec: dict[str, Any] | None = None,
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        libraries: list[dict[str, Any]] | None = None,
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        depends_on: list[str] | None = None,
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        **kwargs
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    ) -> None:
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        """
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        Individual task within a Databricks workflow.
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        Args:
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            task_config: Complete task configuration for Databricks
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            databricks_conn_id: Airflow connection ID for Databricks
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            timeout_seconds: Task-specific timeout override
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            retries: Task-specific retry count override
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            cluster_spec: Cluster configuration for this specific task
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            libraries: Libraries to install for this task
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            depends_on: List of task keys this task depends on
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        """
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```
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## Usage Examples
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### Basic Workflow Creation
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Create a simple multi-stage data pipeline workflow:
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```python { .api }
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from airflow.providers.databricks.operators.databricks_workflow import (
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    DatabricksWorkflowTaskGroup,
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    DatabricksTaskOperator
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)
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# Define workflow with multiple dependent tasks
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with DatabricksWorkflowTaskGroup(
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    group_id='data_pipeline_workflow',
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    databricks_conn_id='databricks_production',
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    max_concurrent_runs=3,
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    default_task_timeout_seconds=3600
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) as data_pipeline:
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    # Extract raw data
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    extract_task = DatabricksTaskOperator(
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        task_id='extract_data',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/pipelines/extract/daily_extract',
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                'source': 'WORKSPACE',
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                'base_parameters': {
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                    'extraction_date': '{{ ds }}',
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                    'source_systems': 'crm,billing,support',
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                    'output_path': '/raw/daily/{{ ds }}'
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                }
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            },
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            'new_cluster': {
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                'spark_version': '12.2.x-scala2.12',
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                'node_type_id': 'i3.large',
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                'num_workers': 3,
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                'spark_conf': {
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                    'spark.sql.adaptive.enabled': 'true'
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                }
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            }
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        }
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    )
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    # Transform and clean data
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    transform_task = DatabricksTaskOperator(
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        task_id='transform_data',
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        task_config={
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            'spark_python_task': {
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                'python_file': 'dbfs:/pipelines/transform/data_cleaner.py',
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                'parameters': [
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                    '--input-path', '/raw/daily/{{ ds }}',
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                    '--output-path', '/processed/daily/{{ ds }}',
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                    '--quality-threshold', '0.95'
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                ]
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            },
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            'job_cluster_key': 'transform_cluster'
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        },
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        depends_on=['extract_data']
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    )
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    # Load into data warehouse
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    load_task = DatabricksTaskOperator(
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        task_id='load_to_warehouse',
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        task_config={
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            'sql_task': {
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                'query': {
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                    'query_id': 'warehouse-load-query-123'
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                },
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                'warehouse_id': 'analytics-warehouse-001',
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                'parameters': {
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                    'process_date': '{{ ds }}',
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                    'source_path': '/processed/daily/{{ ds }}'
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                }
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            }
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        },
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        depends_on=['transform_data']
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    )
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    # Generate reports
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    reporting_task = DatabricksTaskOperator(
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        task_id='generate_reports',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/reporting/daily_dashboard',
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                'base_parameters': {
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                    'report_date': '{{ ds }}',
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                    'dashboard_refresh': 'true'
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                }
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            },
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            'existing_cluster_id': 'reporting-cluster-001'
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        },
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        depends_on=['load_to_warehouse']
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    )
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    # Define workflow dependencies
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    extract_task >> transform_task >> load_task >> reporting_task
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```
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### Complex Multi-Branch Workflow
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Create workflows with parallel branches and conditional execution:
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```python { .api }
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with DatabricksWorkflowTaskGroup(
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    group_id='ml_pipeline_workflow',
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    databricks_conn_id='databricks_ml',
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    existing_clusters={
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        'feature_cluster': 'feature-engineering-001',
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        'training_cluster': 'ml-training-gpu-001',
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        'inference_cluster': 'ml-inference-001'
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    },
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    max_concurrent_runs=2
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) as ml_pipeline:
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    # Feature engineering tasks (parallel)
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    customer_features = DatabricksTaskOperator(
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        task_id='extract_customer_features',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/ml/features/customer_features',
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                'base_parameters': {
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                    'feature_date': '{{ ds }}',
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                    'lookback_days': '90'
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                }
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            },
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            'job_cluster_key': 'feature_cluster'
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        }
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    )
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    product_features = DatabricksTaskOperator(
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        task_id='extract_product_features',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/ml/features/product_features',
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                'base_parameters': {
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                    'feature_date': '{{ ds }}',
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                    'category_encoding': 'onehot'
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                }
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            },
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            'job_cluster_key': 'feature_cluster'
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        }
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    )
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    interaction_features = DatabricksTaskOperator(
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        task_id='extract_interaction_features',
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        task_config={
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            'spark_python_task': {
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                'python_file': 'dbfs:/ml/features/interaction_builder.py',
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                'parameters': [
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                    '--date', '{{ ds }}',
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                    '--interaction-types', 'customer_product,temporal',
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                    '--output-format', 'delta'
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                ]
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            },
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            'job_cluster_key': 'feature_cluster'
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        }
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    )
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    # Feature combination and validation
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    combine_features = DatabricksTaskOperator(
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        task_id='combine_features',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/ml/features/feature_combiner',
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                'base_parameters': {
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                    'feature_date': '{{ ds }}',
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                    'validation_split': '0.2',
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                    'target_column': 'conversion_probability'
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                }
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            },
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            'job_cluster_key': 'feature_cluster'
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        },
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        depends_on=['extract_customer_features', 'extract_product_features', 'extract_interaction_features']
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    )
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    # Model training (conditional on feature validation)
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    train_model = DatabricksTaskOperator(
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        task_id='train_model',
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        task_config={
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            'python_wheel_task': {
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                'package_name': 'ml_training_package',
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                'entry_point': 'train_recommender_model',
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                'parameters': [
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                    '--training-data-path', '/features/combined/{{ ds }}',
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                    '--model-output-path', '/models/recommender/{{ ds }}',
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                    '--hyperopt-trials', '100',
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                    '--early-stopping', 'true'
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                ]
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            },
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            'new_cluster': {
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                'spark_version': '12.2.x-cpu-ml-scala2.12',
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                'node_type_id': 'i3.4xlarge',
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                'num_workers': 5,
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                'spark_conf': {
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                    'spark.task.maxFailures': '3'
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                }
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            },
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            'libraries': [
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                {'pypi': {'package': 'mlflow>=2.0.0'}},
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                {'pypi': {'package': 'hyperopt>=0.2.0'}},
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                {'pypi': {'package': 'xgboost>=1.6.0'}}
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            ]
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        },
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        depends_on=['combine_features']
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    )
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    # Model validation and deployment
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    validate_model = DatabricksTaskOperator(
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        task_id='validate_model',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/ml/validation/model_validator',
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                'base_parameters': {
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                    'model_path': '/models/recommender/{{ ds }}',
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                    'validation_data_path': '/features/validation/{{ ds }}',
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                    'performance_threshold': '0.85',
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                    'deployment_environment': 'staging'
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                }
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            },
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            'job_cluster_key': 'inference_cluster'
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        },
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        depends_on=['train_model']
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    )
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    # Deploy to production (conditional on validation success)
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    deploy_model = DatabricksTaskOperator(
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        task_id='deploy_to_production',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/ml/deployment/model_deployer',
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                'base_parameters': {
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                    'model_path': '/models/recommender/{{ ds }}',
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                    'deployment_target': 'production',
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                    'canary_percentage': '10',
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                    'rollback_threshold': '0.80'
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                }
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            },
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            'existing_cluster_id': 'production-deployment-001'
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        },
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        depends_on=['validate_model']
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    )
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    # Set up dependencies
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    [customer_features, product_features, interaction_features] >> combine_features
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    combine_features >> train_model >> validate_model >> deploy_model
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```
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### Workflow with Shared Job Clusters
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Define workflows that share cluster resources across multiple tasks:
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```python { .api }
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with DatabricksWorkflowTaskGroup(
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    group_id='shared_cluster_workflow',
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    databricks_conn_id='databricks_etl',
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    extra_job_params={
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        'job_clusters': [
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            {
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                'job_cluster_key': 'etl_cluster',
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                'new_cluster': {
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                    'spark_version': '12.2.x-scala2.12',
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                    'node_type_id': 'i3.xlarge',
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                    'num_workers': 8,
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                    'autoscale': {
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                        'min_workers': 2,
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                        'max_workers': 10
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                    },
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                    'spark_conf': {
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                        'spark.sql.adaptive.enabled': 'true',
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                        'spark.sql.adaptive.coalescePartitions.enabled': 'true'
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                    }
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                }
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            },
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            {
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                'job_cluster_key': 'analytics_cluster',
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                'new_cluster': {
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                    'spark_version': '12.2.x-scala2.12',
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                    'node_type_id': 'r5.2xlarge',
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                    'num_workers': 4,
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                    'spark_conf': {
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                        'spark.sql.execution.arrow.pyspark.enabled': 'true'
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                    }
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                }
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            }
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        ],
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        'libraries': [
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            {'pypi': {'package': 'pandas>=1.5.0'}},
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            {'pypi': {'package': 'numpy>=1.21.0'}},
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            {'maven': {'coordinates': 'org.apache.spark:spark-avro_2.12:3.3.0'}}
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        ]
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    }
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) as shared_workflow:
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    # ETL tasks using shared ETL cluster
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    extract_customers = DatabricksTaskOperator(
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        task_id='extract_customers',
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        task_config={
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            'spark_python_task': {
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                'python_file': 'dbfs:/etl/extractors/customer_extractor.py',
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                'parameters': ['--date', '{{ ds }}', '--format', 'delta']
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            },
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            'job_cluster_key': 'etl_cluster'
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        }
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    )
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    extract_orders = DatabricksTaskOperator(
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        task_id='extract_orders',
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        task_config={
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            'spark_python_task': {
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                'python_file': 'dbfs:/etl/extractors/order_extractor.py',
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                'parameters': ['--date', '{{ ds }}', '--include-cancelled', 'false']
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            },
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            'job_cluster_key': 'etl_cluster'
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        }
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    )
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    # Data joining and transformation
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    join_data = DatabricksTaskOperator(
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        task_id='join_customer_orders',
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        task_config={
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            'notebook_task': {
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                'notebook_path': '/etl/transformers/customer_order_joiner',
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                'base_parameters': {
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                    'process_date': '{{ ds }}',
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                    'join_strategy': 'broadcast_hash',
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                    'output_partitions': '100'
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                }
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            },
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            'job_cluster_key': 'etl_cluster'
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        },
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        depends_on=['extract_customers', 'extract_orders']
414
    )
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    # Analytics tasks using dedicated analytics cluster
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    customer_analytics = DatabricksTaskOperator(
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        task_id='customer_segmentation',
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        task_config={
420
            'notebook_task': {
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                'notebook_path': '/analytics/customer_segmentation',
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                'base_parameters': {
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                    'analysis_date': '{{ ds }}',
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                    'segmentation_method': 'kmeans',
425
                    'num_clusters': '5'
426
                }
427
            },
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            'job_cluster_key': 'analytics_cluster'
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        },
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        depends_on=['join_customer_orders']
431
    )
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    revenue_analytics = DatabricksTaskOperator(
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        task_id='revenue_analysis',
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        task_config={
436
            'sql_task': {
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                'query': {
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                    'query_id': 'revenue-analysis-query'
439
                },
440
                'warehouse_id': 'analytics-warehouse'
441
            }
442
        },
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        depends_on=['join_customer_orders']
444
    )
445
    
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    # Define workflow structure
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    [extract_customers, extract_orders] >> join_data >> [customer_analytics, revenue_analytics]
448
```
449

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### Git-Integrated Workflow
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Execute workflows using code from Git repositories:
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```python { .api }
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with DatabricksWorkflowTaskGroup(
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    group_id='git_integrated_workflow',
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    databricks_conn_id='databricks_dev',
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    extra_job_params={
459
        'git_source': {
460
            'git_url': 'https://github.com/company/data-pipelines.git',
461
            'git_branch': '{{ params.git_branch | default("main") }}',
462
            'git_provider': 'gitHub'
463
        }
464
    }
465
) as git_workflow:
466
    
467
    # Data validation using Git-stored notebooks
468
    validate_inputs = DatabricksTaskOperator(
469
        task_id='validate_input_data',
470
        task_config={
471
            'notebook_task': {
472
                'notebook_path': 'validation/input_validator.py',
473
                'source': 'GIT',
474
                'base_parameters': {
475
                    'validation_date': '{{ ds }}',
476
                    'strict_mode': 'true'
477
                }
478
            },
479
            'existing_cluster_id': 'validation-cluster-001'
480
        }
481
    )
482
    
483
    # ETL processing
484
    process_data = DatabricksTaskOperator(
485
        task_id='process_data',
486
        task_config={
487
            'spark_python_task': {
488
                'python_file': 'processing/daily_processor.py',
489
                'source': 'GIT',
490
                'parameters': [
491
                    '--config', 'configs/production.yaml',
492
                    '--date', '{{ ds }}',
493
                    '--parallel-jobs', '4'
494
                ]
495
            },
496
            'new_cluster': {
497
                'spark_version': '12.2.x-scala2.12',
498
                'node_type_id': 'i3.2xlarge',
499
                'num_workers': 6
500
            }
501
        },
502
        depends_on=['validate_input_data']
503
    )
504
    
505
    # Quality assessment
506
    assess_quality = DatabricksTaskOperator(
507
        task_id='assess_data_quality',
508
        task_config={
509
            'python_wheel_task': {
510
                'package_name': 'data_quality_package',
511
                'entry_point': 'run_quality_checks',
512
                'parameters': [
513
                    '--data-path', '/processed/{{ ds }}',
514
                    '--rules-config', 'quality_rules.json'
515
                ]
516
            },
517
            'existing_cluster_id': 'quality-cluster-001'
518
        },
519
        depends_on=['process_data']
520
    )
521

522
    validate_inputs >> process_data >> assess_quality
523
```
524

525
## Advanced Workflow Features
526

527
### Conditional Task Execution
528

529
Implement conditional logic within workflows:
530

531
```python { .api }
532
with DatabricksWorkflowTaskGroup(
533
    group_id='conditional_workflow',
534
    databricks_conn_id='databricks_conditional'
535
) as conditional_workflow:
536
    
537
    # Check data availability
538
    check_data = DatabricksTaskOperator(
539
        task_id='check_data_availability',
540
        task_config={
541
            'notebook_task': {
542
                'notebook_path': '/checks/data_availability_checker',
543
                'base_parameters': {
544
                    'check_date': '{{ ds }}',
545
                    'required_sources': 'sales,marketing,customer_service'
546
                }
547
            },
548
            'existing_cluster_id': 'utility-cluster-001'
549
        }
550
    )
551
    
552
    # Full processing (when all data available)
553
    full_processing = DatabricksTaskOperator(
554
        task_id='full_data_processing',
555
        task_config={
556
            'notebook_task': {
557
                'notebook_path': '/processing/full_pipeline',
558
                'base_parameters': {
559
                    'process_date': '{{ ds }}',
560
                    'processing_mode': 'complete'
561
                }
562
            },
563
            'job_cluster_key': 'processing_cluster'
564
        },
565
        depends_on=['check_data_availability']
566
    )
567
    
568
    # Partial processing (when some data missing)
569
    partial_processing = DatabricksTaskOperator(
570
        task_id='partial_data_processing',
571
        task_config={
572
            'notebook_task': {
573
                'notebook_path': '/processing/partial_pipeline',
574
                'base_parameters': {
575
                    'process_date': '{{ ds }}',
576
                    'processing_mode': 'available_only'
577
                }
578
            },
579
            'job_cluster_key': 'processing_cluster'
580
        },
581
        depends_on=['check_data_availability']
582
    )
583
    
584
    check_data >> [full_processing, partial_processing]
585
```
586

587
### Workflow with Error Handling
588

589
Implement comprehensive error handling and recovery:
590

591
```python { .api }
592
with DatabricksWorkflowTaskGroup(
593
    group_id='resilient_workflow',
594
    databricks_conn_id='databricks_production',
595
    default_task_retries=2,
596
    default_task_timeout_seconds=7200
597
) as resilient_workflow:
598
    
599
    # Critical data processing with retry logic
600
    critical_processing = DatabricksTaskOperator(
601
        task_id='critical_data_processing',
602
        task_config={
603
            'spark_python_task': {
604
                'python_file': 'dbfs:/critical/data_processor.py',
605
                'parameters': ['--date', '{{ ds }}', '--retry-mode', 'true']
606
            },
607
            'new_cluster': {
608
                'spark_version': '12.2.x-scala2.12',
609
                'node_type_id': 'i3.xlarge',
610
                'num_workers': 4
611
            }
612
        },
613
        retries=3,
614
        timeout_seconds=3600
615
    )
616
    
617
    # Backup processing (runs if critical processing fails)
618
    backup_processing = DatabricksTaskOperator(
619
        task_id='backup_data_processing',
620
        task_config={
621
            'notebook_task': {
622
                'notebook_path': '/backup/alternative_processor',
623
                'base_parameters': {
624
                    'process_date': '{{ ds }}',
625
                    'fallback_mode': 'true'
626
                }
627
            },
628
            'existing_cluster_id': 'backup-cluster-001'
629
        },
630
        depends_on=['critical_data_processing']
631
    )
632
    
633
    # Notification task (always runs)
634
    notify_completion = DatabricksTaskOperator(
635
        task_id='notify_completion',
636
        task_config={
637
            'notebook_task': {
638
                'notebook_path': '/notifications/workflow_notifier',
639
                'base_parameters': {
640
                    'workflow_id': '{{ run_id }}',
641
                    'completion_date': '{{ ds }}',
642
                    'status': 'completed'
643
                }
644
            },
645
            'existing_cluster_id': 'utility-cluster-001'
646
        },
647
        depends_on=['backup_data_processing']
648
    )
649
    
650
    critical_processing >> backup_processing >> notify_completion
651
```
652

653
## Monitoring and Troubleshooting
654

655
### Workflow Status Monitoring
656

657
Monitor workflow execution with custom status checks:
658

659
```python { .api }
660
from airflow.providers.databricks.sensors.databricks import DatabricksSensor
661

662
def monitor_workflow_execution(**context):
663
    """Custom monitoring function for workflow status."""
664
    workflow_run_id = context['ti'].xcom_pull(task_ids='data_pipeline_workflow', key='run_id')
665
    
666
    # Custom monitoring logic
667
    print(f"Monitoring workflow run: {workflow_run_id}")
668
    
669
    return workflow_run_id
670

671
# Workflow with monitoring
672
workflow_monitor = DatabricksSensor(
673
    task_id='monitor_workflow_completion',
674
    run_id="{{ task_instance.xcom_pull(task_ids='data_pipeline_workflow', key='run_id') }}",
675
    databricks_conn_id='databricks_production',
676
    poke_interval=60,
677
    timeout=7200,
678
    deferrable=True
679
)
680

681
data_pipeline >> workflow_monitor
682
```
683

684
### Resource Usage Optimization
685

686
Optimize workflow resource allocation:
687

688
```python { .api }
689
with DatabricksWorkflowTaskGroup(
690
    group_id='optimized_workflow',
691
    databricks_conn_id='databricks_optimized',
692
    extra_job_params={
693
        'job_clusters': [
694
            {
695
                'job_cluster_key': 'small_tasks',
696
                'new_cluster': {
697
                    'spark_version': '12.2.x-scala2.12',
698
                    'node_type_id': 'i3.large',
699
                    'autoscale': {'min_workers': 1, 'max_workers': 3}
700
                }
701
            },
702
            {
703
                'job_cluster_key': 'large_tasks', 
704
                'new_cluster': {
705
                    'spark_version': '12.2.x-scala2.12',
706
                    'node_type_id': 'i3.2xlarge',
707
                    'autoscale': {'min_workers': 4, 'max_workers': 12}
708
                }
709
            }
710
        ],
711
        'timeout_seconds': 14400,
712
        'max_concurrent_runs': 3
713
    }
714
) as optimized_workflow:
715
    
716
    # Light preprocessing on small cluster
717
    light_preprocessing = DatabricksTaskOperator(
718
        task_id='light_preprocessing',
719
        task_config={
720
            'notebook_task': {
721
                'notebook_path': '/preprocessing/light_cleaner'
722
            },
723
            'job_cluster_key': 'small_tasks'
724
        },
725
        timeout_seconds=1800
726
    )
727
    
728
    # Heavy computation on large cluster
729
    heavy_computation = DatabricksTaskOperator(
730
        task_id='heavy_computation',
731
        task_config={
732
            'spark_python_task': {
733
                'python_file': 'dbfs:/compute/heavy_aggregator.py'
734
            },
735
            'job_cluster_key': 'large_tasks'
736
        },
737
        timeout_seconds=10800,
738
        depends_on=['light_preprocessing']
739
    )
740
    
741
    light_preprocessing >> heavy_computation
742
```
743

744
The workflow orchestration capabilities provide powerful tools for creating complex, multi-task pipelines that leverage Databricks' native workflow engine while maintaining full integration with Airflow's scheduling, monitoring, and error handling systems.