Great Expectations Databricks


      Great Expectations Databricks

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:


    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”],
  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_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 =
  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.

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