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: Extracting Lineage from Logs
Was this section helpful?
- re - Regular expression operations, Python Software Foundation, 2024 - Essential guide to Python's built-in module for working with regular expressions, directly applicable to the log parsing exercise.
- Data Lineage Management in Big Data Environments: A Review, José D. Hernández-Cruz, Ricardo V. Teixeira, Flávio R. C. Fernandes and Carlos H. N. E. Costa, 2019 Computing, Vol. 101 (Springer Vienna) DOI: 10.1007/s00607-019-00778-9 - A comprehensive review of techniques and challenges for managing data lineage in large-scale data systems, providing context for the importance of lineage extraction.
- OpenLineage Documentation, LF AI & Data Foundation, 2024 (LF AI & Data Foundation) - Official documentation for the OpenLineage standard, representing a modern approach to collecting and exchanging data lineage metadata programmatically.
- Data Pipelines Pocket Reference: Moving and Processing Data for Robust Analytics, James Densmore, 2021 (O'Reilly Media) - A practical guide to designing and implementing data pipelines, which implicitly covers the need for understanding data flow and dependencies, providing context for lineage.
© 2025 ApX Machine LearningEngineered with