EvaluateDataQuality class - AWS Glue

EvaluateDataQuality class

AWS Glue Data Quality is in preview release for AWS Glue and is subject to change.

Package: com.amazonaws.services.glue.dq

object EvaluateDataQuality

Def apply

def apply(frame: DynamicFrame, ruleset: String, publishingOptions: JsonOptions = JsonOptions.empty): DynamicFrame

Evaluates a data quality ruleset against a DynamicFrame, and returns a new DynamicFrame with results of the evaluation. To learn more about AWS Glue Data Quality, see AWS Glue Data Quality.

  • frame – The DynamicFrame that you want to evaluate the data quality of.

  • ruleset – A Data Quality Definition Language (DQDL) ruleset in string format. To learn more about DQDL, see the Data Quality Definition Language (DQDL) reference guide.

  • publishingOptions – A dictionary that specifies the following options for publishing evaluation results and metrics:

    • dataQualityEvaluationContext – A string that specifies the namespace under which AWS Glue should publish Amazon CloudWatch metrics and the data quality results. The aggregated metrics appear in CloudWatch, while the full results appear in the AWS Glue Studio interface.

      • Required: No

      • Default value: default_context

    • enableDataQualityCloudWatchMetrics – Specifies whether the results of the data quality evaluation should be published to CloudWatch. You specify a namespace for the metrics using the dataQualityEvaluationContext option.

      • Required: No

      • Default value: False

    • enableDataQualityResultsPublishing – Specifies whether the data quality results should be visible on the Data Quality tab in the AWS Glue Studio interface.

      • Required: No

      • Default value: True

    • resultsS3Prefix – Specifies the Amazon S3 location where AWS Glue can write the data quality evaluation results.

      • Required: No

      • Default value: "" (empty string)


The following example code demonstrates how to evaluate data quality for a DynamicFrame before performing a SelectFields transform. The script verifies that all data quality rules pass before it attempts the transform.

import com.amazonaws.services.glue.GlueContext import com.amazonaws.services.glue.MappingSpec import com.amazonaws.services.glue.errors.CallSite import com.amazonaws.services.glue.util.GlueArgParser import com.amazonaws.services.glue.util.Job import com.amazonaws.services.glue.util.JsonOptions import org.apache.spark.SparkContext import scala.collection.JavaConverters._ import com.amazonaws.services.glue.dq.EvaluateDataQuality object GlueApp { def main(sysArgs: Array[String]) { val spark: SparkContext = new SparkContext() val glueContext: GlueContext = new GlueContext(spark) // @params: [JOB_NAME] val args = GlueArgParser.getResolvedOptions(sysArgs, Seq("JOB_NAME").toArray) Job.init(args("JOB_NAME"), glueContext, args.asJava) // Create DynamicFrame with data val Legislators_Area = glueContext.getCatalogSource(database="legislators", tableName="areas_json", transformationContext="S3bucket_node1").getDynamicFrame() // Define data quality ruleset val DQ_Ruleset = """ Rules = [ColumnExists "id"] """ // Evaluate data quality val DQ_Results = EvaluateDataQuality.apply(frame=Legislators_Area, ruleset=DQ_Ruleset, publishingOptions=JsonOptions("""{"dataQualityEvaluationContext": "Legislators_Area", "enableDataQualityMetrics": "true", "enableDataQualityResultsPublishing": "true"}""")) assert(DQ_Results.filter(_.getField("Outcome").contains("Failed")).count == 0, "Failing DQ rules for Legislators_Area caused the job to fail.") // Script generated for node Select Fields val SelectFields_Results = Legislators_Area.selectFields(paths=Seq("id", "name"), transformationContext="Legislators_Area") Job.commit() } }