def process_payroll(_, df): return len(df) @solid(input_defs=[ InputDefinition(name='numrows'), InputDefinition(name='expectation') ]) def postprocess_payroll(_, numrows, expectation): if expectation["success"]: return numrows else: raise ValueError payroll_expectations = ge_validation_solid_factory("getest", "basic.warning") @pipeline( mode_defs=[ ModeDefinition('basic', resource_defs={'ge_data_context': ge_data_context}) ], preset_defs=[ PresetDefinition( 'sample_preset_success', mode='basic', run_config={ 'resources': { 'ge_data_context': { 'config': {
def process_payroll(_, df): return len(df) @solid(input_defs=[ InputDefinition(name="numrows"), InputDefinition(name="expectation") ]) def postprocess_payroll(_, numrows, expectation): if expectation["success"]: return numrows else: raise ValueError payroll_expectations = ge_validation_solid_factory(datasource_name="getest", suite_name="basic.warning") @pipeline( mode_defs=[ ModeDefinition("basic", resource_defs={"ge_data_context": ge_data_context}) ], preset_defs=[ PresetDefinition( "sample_preset_success", mode="basic", run_config={ "resources": { "ge_data_context": { "config": {
def hello_world_pandas_pipeline_v2(): return reyielder( ge_validation_solid_factory("ge_validation_solid", "getest", "basic.warning")(pandas_yielder()))
def hello_world_pyspark_pipeline(): validate = ge_validation_solid_factory( "getestspark", "basic.warning", input_dagster_type=DagsterPySparkDataFrame) return reyielder(validate(pyspark_yielder()))