A company builds a new data pipeline to process data for business intelligence reports.

Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.

Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialIntegrity rule for each join.

Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested columns contain null values. Use a second SQL transform to check referential integrity.

Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.