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
Anatomy of a Data Assertion
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- The DAMA Guide to the Data Management Body of Knowledge (DMBOK2), DAMA International, 2017 (Technics Publications) - This comprehensive guide provides a standard framework for data management, including detailed sections on data quality and governance, essential for understanding the context and purpose of data assertions.
- Fundamentals of Data Engineering, Joe Reis, and Matt Housley, 2022 (O'Reilly Media) - A practical guide for building production-ready data systems, covering data quality, testing methodologies, and architectural patterns for robust data pipelines, directly relevant to implementing assertions.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Offers foundational principles for building reliable data systems, providing the broader context for why data quality assertions are critical for data consistency and system integrity.
- Automatic Data Validation using Semantic Dependencies, Ali Abed, Carsten Binnig, and Ahmad Khan, 2020 Proceedings of the VLDB Endowment, Vol. 13 (VLDB Endowment Inc.) DOI: 10.14778/3407881.3407945 - This research paper introduces methods for automated data validation, aligning with the formal definition and implementation of assertions as predicate functions in data pipelines.
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