Implement Data Quality Monitoring

Step 1: Install and configure Great Expectations

Set up Great Expectations in your Python environment and create a data context for your project.

pip install great-expectations
great_expectations init

Step 2: Define data quality expectations

Create expectation suites for your key datasets. Start with basic checks like non-null values, value ranges, and uniqueness.

Step 3: Integrate with your pipeline

Add validation checkpoints to your data pipeline. Configure alerts to notify your team when data quality issues are detected.

Step 4: Build a monitoring dashboard

Create a dashboard to visualize data quality metrics over time. Track freshness, completeness, and accuracy trends.

Step 5: Set up automated alerts

Configure email or Slack notifications for critical data quality failures. Define severity levels and escalation paths.