EvaluateDataQuality class - Amazon Glue
applyExample
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EvaluateDataQuality class

Amazon Glue Data Quality is in preview release for Amazon 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 Amazon Glue Data Quality, see Amazon 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 Amazon Glue should publish Amazon CloudWatch metrics and the data quality results. The aggregated metrics appear in CloudWatch, while the full results appear in the Amazon 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 Amazon Glue Studio interface.

      • Required: No

      • Default value: True

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

      • Required: No

      • Default value: "" (empty string)

Example

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()
  }
}