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
Impact Analysis Techniques
Was this section helpful?
- Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2022 (MIT Press) - Provides fundamental coverage of graph theory and algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS), which are essential for understanding graph reachability in impact analysis.
- DataHub: A Metadata Platform for the Modern Data Stack, Shirshanka Das, Sasha Markine, Steven C. Chu, Kevin Kho, Mars Lan, Jeremy Lou, Joshua P. S. Park, Mark M. Sanchez, David Shriver, Ryan J. R. Smith, Shreya Singh, Mike S. K. Wang, Hao Yang, 2020 Proceedings of the VLDB Endowment (PVLDB), Vol. 13 (VLDB Endowment Inc.) DOI: 10.14778/3407913.3407929 - Describes the architecture and implementation of a scalable metadata platform with robust data lineage capabilities, which serves as the foundation for practical impact analysis in production environments.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Discusses critical aspects of schema evolution, data compatibility, and the challenges of managing changes in distributed data systems, providing essential context for understanding schema drift and its impact.
- Data Observability: A Practitioner's Guide to Reliable Data, Barr Moses, Lior Gavish, 2023 (O'Reilly Media) - Explores methods for detecting and addressing data quality issues, including schema changes, data drift, and semantic anomalies, which are crucial for comprehensive impact analysis and ensuring data reliability.
© 2025 ApX Machine LearningEngineered with