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
Practice: Writing Data Validation Suites
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- Data Contracts: Define, Test & Monitor Data Quality, Soda, 2025 (Soda) - Describes data contracts as a policy-as-code approach to data quality, directly mapping to the 'Contract (Configuration)' component of validation suites.
- Fundamentals of Data Engineering: Planning and Building Robust Data Systems, Joe Reis and Matt Housley, 2022 (O'Reilly Media) - Provides a comprehensive treatment of data quality, governance, and observability within the context of modern data engineering practices and system design.
- Data quality assessment: A survey, Carlo Batini and Cinzia Scannapieco, 2016 ACM Computing Surveys, Vol. 49 (ACM) DOI: 10.1145/2967118 - A comprehensive survey reviewing various data quality dimensions, assessment techniques, and related frameworks, providing academic context for data validation.
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