I’m using Great Expectations 1.8.0 inside an Apache Airflow DAG (Python 3.12), and I keep getting this error whenever i trigger it by my dag InvalidDataContextConfigError: Error while processing DataContextConfig: anonymous_usage_statistics validations_store_name evaluation_parameter_store_name [2025-11-04, 15:03:28] ERROR - Error while processing DataContextConfig: anonymous_usage_statistics validations_store_name evaluation_parameter_store_name InvalidDataContextConfigError: Error while processing DataContextConfig: anonymous_usage_statistics validations_store_name evaluation_parameter_store_name File "/home/airflow/.local/lib/python3.12/site-packages/great_expectations/data_context/data_context/file_data_context.py", line 63 in init File "/home/airflow/.local/lib/python3.12/site-packages/great_expectations/data_context/data_context/file_data_context.py", line 114 in _init_project_config File "/home/airflow/.local/lib/python3.12/site-packages/great_expectations/data_context/types/base.py", line 1148 in handle_error THIS IS MY Great_Expectations.yml file

great_expectations.yml — GE 1.8.x (Azure Blob + local checkpoint)

config_version: 3.0 config_variables_file_path: uncommitted/config_variables.yml plugins_directory: plugins/

1.8 requires a mapping, not a bare boolean

anonymous_usage_statistics: enabled: true

data_context_id optional; rakhna ho to theek hai:

data_context_id: 00000000-0000-0000-0000-00000000e003

Purana (empty) datasources section hata do

datasources: {}

stores: expectations_AZ_store: class_name: ExpectationsStore store_backend: class_name: TupleAzureBlobStoreBackend container: "$web" # <-- FIX: no backslash prefix: expectations connection_string: ${AZURE_STORAGE_CONNECTION_STRING} validations_AZ_store: class_name: ValidationsStore store_backend: class_name: TupleAzureBlobStoreBackend container: "$web" # <-- FIX: no backslash prefix: validations connection_string: ${AZURE_STORAGE_CONNECTION_STRING} evaluation_parameter_store: class_name: EvaluationParameterStore

Tum pehle local filesystem checkpoint store use kar rahe the — wahi rakha hai

checkpoint_store: class_name: CheckpointStore store_backend: class_name: TupleFilesystemStoreBackend suppress_store_backend_id: true base_directory: checkpoints/

(Optional) Profiler store — jo tum use kar rahe the, rakha hai

profiler_store: class_name: ProfilerStore store_backend: class_name: TupleFilesystemStoreBackend suppress_store_backend_id: true base_directory: profilers/

Default store name mappings (MUST match keys above exactly)

expectations_store_name: expectations_AZ_store validations_store_name: validations_AZ_store evaluation_parameter_store_name: evaluation_parameter_store checkpoint_store_name: checkpoint_store

Data Docs on Azure ($web)

data_docs_sites: local_site: class_name: SiteBuilder store_backend: class_name: TupleAzureBlobStoreBackend container: "$web" # <-- FIX: no backslash connection_string: ${AZURE_STORAGE_CONNECTION_STRING} site_index_builder: class_name: DefaultSiteIndexBuilder

Fluent datasources ko YAML me rakhna optional hai; best practice: code se add karo.

Agar rakhna hi hai to yaha rehne do; 1.8 isko ignore nahi karta.

fluent_datasources: my_spark_in_memory_datasource: type: spark assets: unique_patient_new: type: dataframe batch_metadata: {} my_pandas_in_memory_datasource: type: pandas assets: alis_patient: type: dataframe batch_metadata: {} exer_data_gx: type: pandas assets: exer_data: type: dataframe batch_metadata: {} silver_patient_df: type: pandas assets: silver_patient_asset: type: dataframe batch_metadata: {} silver_patients: type: pandas assets: silver_patients_asset: type: dataframe batch_metadata: {} exer_fake: type: pandas assets: exer_fake_asset: type: dataframe batch_metadata: {} exer_data: type: pandas assets: exer_data_asset: type: dataframe batch_metadata: {} exer_df_datasource: type: pandas assets: exer_df_asset: type: dataframe batch_metadata: {} exer_data_datasource: type: pandas assets: exer_data_asset: type: dataframe batch_metadata: {} exergait_datasource: type: pandas assets: exergait_asset: type: dataframe batch_metadata: {} silver_patient_datasource: type: pandas assets: silver_patient_asset: type: dataframe batch_metadata: {} What I’ve tried Verified that great_expectations.yml exists and is being loaded from: /mnt/azurecodefiles/data_pipelines/airflow/great_expectations/ Restarted Airflow scheduler and worker after editing YAML Still getting the same error. Question What exactly causes this InvalidDataContextConfigError for anonymous_usage_statistics, validations_store_name, and evaluation_parameter_store_name in Great Expectations 1.8? Is there a schema change in 1.8 that requires different keys or structure in great_expectations.yml? Would appreciate if someone could share a valid GE 1.8 YAML example (especially with Azure Blob stores) or explain what’s wrong in this one. Environment Python: 3.12 Great Expectations: 1.8.0 Airflow: 3.1.0 Deployment: Azure-hosted Airflow Dev Environment Backend: Azure Blob Storage