feat: Add Dagster integration (to_dagster_assets, to_dagster_job) #47
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Add support for Dagster as an orchestration target: - to_dagster_assets(): Generate Dagster assets from pipeline - One asset per target table with automatic dependency wiring - Support for group names, key prefixes, compute kinds - Asset metadata with query_id and table name - to_dagster_job(): Generate Dagster job from pipeline (ops-based) - Alternative approach for job-based workflows Also improves Airflow 3.x compatibility: - Auto-detect Airflow version when generating DAGs - Support explicit version specification via airflow_version parameter Includes comprehensive test suite (tests/test_dagster_integration.py) with 16 tests covering asset generation, dependencies, and execution. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Break down large pipeline.py (~2700 lines) using delegation pattern: - Create orchestrators/ package with: - base.py: BaseOrchestrator with shared functionality - airflow.py: AirflowOrchestrator for Airflow DAG generation - dagster.py: DagsterOrchestrator for Dagster assets/jobs - Create execution.py with: - PipelineExecutor for sync/async pipeline execution - Update pipeline.py to delegate to new modules: - to_airflow_dag() → AirflowOrchestrator.to_dag() - to_dagster_assets() → DagsterOrchestrator.to_assets() - to_dagster_job() → DagsterOrchestrator.to_job() - run() → PipelineExecutor.run() - async_run() → PipelineExecutor.async_run() - Export new classes from clgraph.__init__: - AirflowOrchestrator, DagsterOrchestrator, PipelineExecutor Benefits: - Cleaner separation of concerns - Easier to test orchestrators independently - Reduced pipeline.py size (~400 lines removed) - Backward compatible - Pipeline methods still work Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Summary
to_dagster_assets()method to generate Dagster assets from SQL pipelineto_dagster_job()method for ops-based Dagster workflowsChanges
Test plan
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