How do I create AWS Glue Data Quality rules and optimize their performance?

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Implement AWS Glue Data Quality rules and create a ruleset

Use rule recommendations or Data Quality Definition Language (DQDL) to create and define your rules. Then, create a ruleset to group the rules.

Optimize your AWS Glue Data Quality rules

Analyze your data pipeline to collect performance metrics, such as rule execution times, resource utilization, and failure rates. For more information, see Measure performance of AWS Glue Data Quality for extract, transform, and load (ETL) pipelines.

It's a best practice to monitor the performance of your AWS Glue Data Quality rules. Use Amazon CloudWatch to set up AWS Glue Data Quality alerts and notifications.

Review your performance metrics to identify inefficiencies and potential bottlenecks, and then take the following actions:

• Simplify your rules.
• Optimize your data format.
• Partition your data.
• Schedule your rules.

For more information on how to optimize AWS Glue Data Quality rules, see Best Practices.

Simplify your rules

It's a best practice to simplify complex rules that might require more processing power. Complete the following steps:

1. Review your existing AWS Glue Data Quality rules, and then note any rules that involve joins, aggregations, or multiple steps.
2. Split the rules into smaller rules that address specific data quality checks.
3. Implement the simplified rules, and then compare their performance to your previous rules.

Optimize your data format

Determine the data formats in your datasets, and then convert your data to a more efficient format, such as Parquet. Be sure to make any necessary changes to your data pipelines or processing logic. For more information, see File formats and data compression.

Partition your data

Partition your data into columns to allow parallel processing.

Complete the following steps:

1. Analyze the structure and access data patterns to identify partitioning criteria, such as date, location, or product.
2. Modify your data ingestion to partition the data into columns according to the identified criteria.
3. Update your AWS Glue Data Quality rules so that they use the partitioned data structure.

For more information, see Work with partitioned data in AWS Glue.

Schedule your rules

Schedule your Data Quality rules to prioritize by significance, impact, and resource requirements.

Related information

How do I troubleshoot issues with AWS Glue Data Quality rules?