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
Implementing Quality Gates
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- Data Quality: The Field Guide, Thomas C. Redman, 2001 (Digital Press) - A seminal work detailing the fundamental concepts and practical methodologies for managing and improving data quality, which underpins the data validation checks in quality gates.
- DataOps: A New Way to Deliver Value with Data, Christopher Bergh, Michael Stonebraker, and David Loshin, 2020 (O'Reilly Media) - This book applies DevOps principles to the data world, providing practical guidance on automating data pipelines, ensuring data quality, and improving collaboration in data engineering, directly aligning with the section's context.
- Testing and Data Quality in dbt, dbt Labs, 2024 (dbt Labs) - Official documentation from dbt Labs, demonstrating practical implementation strategies for data quality tests, schema validation, and custom assertions within a data transformation framework, highly relevant to Gates 1 and 3.
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