Data Governance, Quality, and Observability in Production
Chapter 1: Foundations of Data Governance Code
Defining Governance in Engineering Terms
Data Classification and Tagging Strategies
Role-Based Access Control Models
Policy-as-Code Architectures
Practice: Implementing Access Policies
Chapter 2: Data Quality Dimensions and Testing
Core Dimensions of Data Quality
Anatomy of a Data Assertion
Validating Schemas and Types
Statistical Profiling and Distribution Checks
Practice: Writing Data Validation Suites
Chapter 3: Data Observability Systems
The Pillars of Data Observability
Monitoring Freshness and Latency
Volume and Row Count Anomalies
Schema Drift Detection
Practice: Building a Freshness Monitor
Chapter 4: Data Lineage and Metadata Management
Static vs Dynamic Lineage
The OpenLineage Standard
Dependency Graph Construction
Impact Analysis Techniques
Practice: Extracting Lineage from Logs
Chapter 5: Automated Reliability in CI Pipelines
Pre-commit Hooks for Data Code
Implementing Quality Gates
Circuit Breakers in Pipelines
Alerting and Incident Management
Practice: Configuring a CI Data Test
Volume and Row Count Anomalies
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- Fundamentals of Data Engineering: Planning and Building Robust Data Systems, Joe Reis and Matt Housley, 2022 (O'Reilly Media) - This book covers essential aspects of modern data engineering, including dedicated discussions on data quality, reliability, and observability, which are central to monitoring data pipelines.
- A Survey of Outlier Detection Methodologies, Varun Chandola, Arindam Banerjee, and Vipin Kumar, 2009 ACM Computing Surveys (CSUR), Vol. 41 (Association for Computing Machinery) DOI: 10.1145/1541880.1541882 - This survey paper provides an overview of various outlier detection techniques, including statistical methods directly applicable to identifying volume and row count anomalies in datasets.
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