Data integration pipelines are crucial for organizations to extract and transform data while ensuring high data quality for accurate business decisions. However, fixed data quality rules are often outdated when business environments change, leading to poor quality data. For example, a data engineer set a rule that daily sales must exceed $1 million, but after sales surpassed $2 million, the threshold was no longer accurate. This outdated rule caused errors that went undetected, resulting in incorrect orders and low inventory.
To address these challenges, AWS Glue Data Quality introduces dynamic rules that automatically adjust to changing business properties, eliminating the need to constantly update static rules. By using the last(k) operator in expressions, dynamic rules allow for historical comparisons to be made with current metrics. For instance, a dynamic rule like RowCount > min(last(3)) will evaluate the current number of rows against the minimum row count from the most recent three runs.
By utilizing dynamic rules, organizations can ensure data quality without the manual effort of updating rules regularly. The process involves setting up an AWS Glue job to measure and monitor data quality using dynamic rules. By configuring the job to evaluate data quality metrics against dynamic rules, organizations can take appropriate actions based on the outcomes.
Setting up resources with AWS CloudFormation, configuring the job on the AWS Glue Studio console, and running the job incrementally to assess data quality rules are crucial steps in implementing dynamic rules. By simulating multiple runs with incremental data, organizations can identify and rectify data quality issues early in the data ingestion process.
Overall, AWS Glue Data Quality offers a reliable solution to measure and monitor data quality in ETL pipelines. With dynamic rules, organizations can detect potential data quality issues upfront and ensure cleaner, more accurate data for downstream analytics. By leveraging dynamic rules, organizations can maintain data integrity, identify anomalies, and build trust in their analytical outputs.
In conclusion, AWS Glue Data Quality’s dynamic rules empower organizations to take control of data quality at scale, enabling them to optimize and enhance their data analytics processes effectively. With dynamic rules, organizations can build trust in their data and leverage advanced data quality capabilities to drive better business outcomes.
Article Source
https://aws.amazon.com/blogs/big-data/get-started-with-aws-glue-data-quality-dynamic-rules-for-etl-pipelines/
