Integrate Great Expectations data quality checks into a data pipeline for automated validation and alerting

Verified steps

1. Initialize a Great Expectations project (great_expectations init) to create the project directory and configure a Data Context; define a Datasource pointing to your warehouse (Snowflake, BigQuery, Spark, or Pandas) using the appropriate connection configuration.
2. Create an Expectation Suite for each critical dataset: use the interactive notebook workflow or the Python API to add expectations (expect_column_values_to_not_be_null, expect_column_values_to_be_between, expect_table_row_count_to_be_between, etc.) informed by profiling the reference dataset.
3. Define a Checkpoint that pairs the Expectation Suite with a Batch Request (a query or table reference) and one or more Actions: UpdateDataDocsAction to regenerate HTML docs, SlackNotificationAction or a custom action to alert on failure.
4. Integrate the Checkpoint into your pipeline: call context.run_checkpoint(checkpoint_name=...) in your Airflow operator, dbt post-hook, or Spark job; the call raises an exception on validation failure if configured, blocking downstream tasks.
5. Store Expectation Suites and Validation Results in a shared backend (GCS, S3, or a database store) so that results are accessible to all pipeline runners and the Data Docs site.
6. Review Data Docs after each run to inspect which expectations passed or failed, drill into failed batches, and refine thresholds based on observed data distributions.