A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?
Creating custom AWS Glue Data Quality rulesets allows you to define precise data quality checks tailored to your specific needs, ensuring data accuracy and consistency. This approach provides the flexibility to implement a wide range of validation rules based on the business requirements, making it the most suitable solution for ensuring that the data meets the required standards.
This option provides the necessary flexibility to define and implement custom validation rules tailored to the company's specific requirements for data accuracy and consistency.
B. Create custom AWS Glue Data Quality rulesets to define specific data quality checks. Custom AWS Glue Data Quality rulesets allow you to define precise data quality checks tailored to your specific needs, ensuring that the data meets the required standards of accuracy and consistency. This approach provides flexibility to implement a wide range of validation rules based on your business requirements.
Only B - makes sense