Data Observability on AWS: A Practitioner's Guide to Building Trustworthy Data Pipelines

Transform your data engineering practice from reactive firefighting to proactive observability with this comprehensive guide to building trustworthy data pipelines on AWS.
What You'll Learn:

• Master Great Expectations for automated data validation and quality checks
• Implement comprehensive monitoring with AWS CloudWatch metrics, logs, and alarms
• Leverage AWS Glue Data Catalog as your central observability hub
• Build automated data quality gates that prevent bad data propagation
• Design self-healing data pipelines with AWS Step Functions
• Create collaborative data quality cultures within your organization

Why This Book Matters: Data quality issues cost organizations millions of dollars annually. This book provides practical, hands-on solutions using AWS-native tools to ensure your data pipelines are reliable, observable, and trustworthy.
What Makes This Different:

• Real-world case studies from e-commerce, financial services, and IoT companies
• Step-by-step implementation guides with code examples
• Advanced patterns for anomaly detection and predictive quality
• Complete CI/CD integration strategies
• Future-ready approaches including AI/ML in data observability

Perfect For:

• Data engineers building production pipelines
• Teams transitioning to cloud-native data architectures
• Organizations seeking to improve data reliability and trust
• Anyone working with AWS data services (Glue, Athena, S3, CloudWatch)

Whether you're a seasoned data professional or new to data observability, this book provides the practical knowledge and tools you need to build data systems that your organization can trust.