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
Validating Schemas and Types
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- The DAMA Guide to the Data Management Body of Knowledge (DMBOK2), DAMA International, 2024 (Technics Publications, LLC) - Provides a framework for data management, including sections on data quality and data governance, which are essential for establishing and enforcing data schemas as contracts.
- Apache Avro™ 1.11.1 Specification, The Apache Software Foundation, 2022 - Describes a data serialization system that relies on schemas, supporting data structures and schema evolution, directly addressing schema drift and nested types.
- Pydantic V2 Documentation, Pydantic Development Team, 2023 - Official documentation for Pydantic, a Python library for data validation and settings management using Python type hints, offering a practical approach to defining and enforcing data schemas in Python applications.
- Data Reliability Engineering, Barr Moses, Lior Gavish, Molly Vorwerck, 2022 (O'Reilly Media, Inc.) - Addresses the engineering challenges of building and maintaining reliable data pipelines, including strategies for preventing data quality issues, managing schema changes, and implementing validation processes.
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