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Data Quality

Automated data quality monitoring with AWS Glue

Continuous data validation and alerting to catch quality issues before they impact models.

What this covers

Deploy comprehensive data quality monitoring that automatically validates datasets, detects anomalies, and alerts teams when data quality degrades.

Implementation trail

Glue job quality checks setup
Automated scheduling with triggers
EventBridge monitoring
SNS alerting configuration
Quality dashboards and reporting

Design comprehensive quality checks

Create Glue ETL jobs that validate null values, duplicates, data freshness, and range validations.
Implement statistical checks for detecting outliers and distribution shifts using PySpark.
Set up custom validation rules for business-specific data quality requirements.

Automate quality job scheduling

Configure Glue triggers to run quality jobs on regular intervals matching data refresh cycles.
Set up jobs to process partitioned data efficiently for large datasets.
Implement proper error handling and retry logic for transient failures.

Implement real-time alerting

Use EventBridge to capture Glue job completion events and quality check failures.
Configure Lambda functions to analyze results and determine alert severity.
Set up SNS topics for different alert types with appropriate escalation paths.

Monitor and visualize quality trends

Create CloudWatch dashboards showing quality metrics over time.
Track data quality scores and trends to identify degradation patterns.
Generate regular quality reports for stakeholders and compliance teams.

Ensure data quality at scale?

We build automated quality monitoring that catches data issues early, preventing model degradation and maintaining stakeholder trust.

Monitor your data quality