Here’s a breakdown of how to integrate Great Expectations with Databricks for data quality and validation within your data pipelines:

Key Concepts

• Great Expectations: A Python-based open-source framework for defining, documenting, and validating expectations about your data.
• Databricks: A cloud-based data engineering, analytics, and machine learning platform. It heavily utilizes Apache Spark for distributed data processing.

Why Use Them Together

• Ensure Data Quality: Great Expectations helps you proactively establish expectations (e.g., column types, ranges, completeness) to catch data quality issues early in your Databricks pipelines.
• Prevent Pipeline Failures:  Data quality checks can prevent unexpected data from breaking downstream processes or compromising model outputs.
• Collaborative Workflow: Great Expectations promotes collaboration by allowing data engineers, analysts, and domain experts to define data expectations together.

Steps for Integration

1. Installation:
2. Bash
3. # Install on your Databricks cluster:
   pip install great_expectations

4. Set up a Data Context:

   Python

   import great_expectations as ge

   context = ge.get_context()

5. Connect to a Datasource:
   • Spark DataFrame:
   • datasource_config = {
     “name”: “my_spark_datasource”,
     “class_name”: “Datasource”,
     “execution_engine”: {
     “class_name”: “SparkDFExecutionEngine”
     },
     “data_connectors”: {
     “default_runtime_data_connector_name”: {
     “class_name”: “RuntimeDataConnector”,
     “batch_identifiers”: [“default_identifier_name”],
     }
     },
     }
     context.add_datasource(**datasource_config)
6. Create Expectations:
7. Python

8. batch_kwargs = {
   “path”: “path/to/your/data”,
   “datasource”: “my_spark_datasource”
   }
   batch = context.get_batch(batch_kwargs, “my_expectation_suite”)

   # Define expectations:
   batch.expect_column_to_exist(“column_name”)
   batch.expect_column_values_to_not_be_null(“column_name”)
   batch.expect_column_values_to_be_in_set(“column_name”, [“valid_value_1”, “valid_value_2”])
   # … add more expectations

9. Run Validation with Checkpoints:
10. Python
11. checkpoint_config = {
    “name”: “my_checkpoint”,
    “class_name”: “Checkpoint”,
    “validations”: [
    {
    “batch_request”: batch_kwargs,
    “expectation_suite_name”: “my_expectation_suite”
    }
    ]
    }
    checkpoint = context.add_checkpoint(**checkpoint_config)
    results = checkpoint.run()
12. Review Results:
    • Great Expectations creates data docs (HTML) to analyze quality checks visually.
    • Access results in Databricks notebooks for insights and investigation.

Key Points and Considerations

• Expectation Suite Organization: Structure your expectations into suites for specific datasets or pipeline stages.
• Version Control:  Version Expectations suites with your code.
• Scheduling Checkpoints: Automate data quality checks within Databricks jobs for regular monitoring.