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: Configuring a CI Data Test
Was this section helpful?
- pytest documentation, pytest Developers, 2024 - The official documentation for pytest, providing comprehensive guides on writing and running tests, fixtures, and assertion handling, directly relevant to the Python testing framework used in the section.
- GitHub Actions Documentation, GitHub Docs, 2024 - The official guide for GitHub Actions, covering workflow syntax, event triggers, environment setup, and secret management, which are key components of the CI pipeline configuration discussed.
- Fundamentals of Data Engineering: Planning and Building Data Systems, Joe Reis, Matt Housley, 2022 (O'Reilly Media) - This book offers a contemporary view on building robust data systems, including sections on data quality, data governance, and the application of software engineering practices like CI/CD to data pipelines.
© 2025 ApX Machine LearningEngineered with