If you are building a Data platform the first consideration should be around data quality, cleaning, traceability, ownership and reporting on quality. If the data is of poor value, the business will not use it, or it will negatively impact clients and downstream applications. Too often firms get excited about building a Data Lake or Data Lakehouse, but forget that the framework for quality, governance and traceability has to be constructed first.
To govern data quality and lineage in AWS effectively, it’s important to establish processes and utilize specific AWS services designed for these purposes. Here’s a summary approach:
1. Implement AWS Data Governance Framework
- Define Data Governance Policies: Clearly define your data governance policies, including data quality standards, compliance requirements, and data lineage tracking.
- Designate Data Stewards: Assign data stewards responsible for overseeing data quality and adherence to governance policies.
2. Use AWS Services for Data Quality
- AWS Glue DataBrew: Use DataBrew for cleaning and normalizing data. It provides visual data preparation tools that can help in improving data quality without writing code.
- Amazon Deequ: For those using Apache Spark, Amazon Deequ is a library built on top of Spark for defining and verifying data quality constraints.
3. Implement Data Lineage
- AWS Glue and AWS Lake Formation: Utilize these services to catalog your data. AWS Glue automatically discovers and profiles your data via crawlers, and AWS Lake Formation provides centralized security and governance controls over your data lakes.
- Amazon Neptune: For complex data lineage requirements, consider using Amazon Neptune, a graph database that can help in visualizing and managing data relationships.
4. Monitor Data Quality and Lineage
- Amazon CloudWatch: Use CloudWatch to monitor operational metrics and logs. It can be integrated with AWS services like AWS Glue to monitor data processing jobs.
- AWS Glue Data Quality: This feature enables you to monitor and report on the quality of your data over time. It helps in identifying trends in data quality.
5. Enforce Data Quality Rules
- AWS Lambda: Use Lambda functions to trigger data quality checks or remediation tasks based on specific events, such as new data arriving in S3 buckets.
- Step Functions: Orchestrate multi-step data processing workflows that include data quality checks and handling.
6. Leverage AWS Security & Compliance Services
- AWS Identity and Access Management (IAM): Secure your data by controlling who can access your AWS resources.
- AWS Config: Use AWS Config to track configurations of your AWS resources, helping in ensuring compliance with governance policies.
7. Continuous Improvement
- Feedback Loops: Establish mechanisms for feedback on data quality and lineage from data consumers to continuously improve data governance practices.
- Regular Audits: Conduct regular audits of your data governance practices using AWS Audit Manager to ensure compliance with internal policies and external regulations.
