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
Monitoring Freshness and Latency
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- Designing Data-Intensive Applications, Martin Kleppmann, 2017 (O'Reilly Media) - A foundational book on the principles of reliable, scalable, and maintainable data systems, covering distributed systems, data consistency, and processing models relevant to understanding data timeliness and latency.
- Streaming Systems: The What, Why, and How of Large-Scale Stream Processing, Tyler Akidau, Slava Chernyak, Reuven Lax, 2017 (O'Reilly Media) - This book provides an in-depth exploration of stream processing, including the fundamental concepts of event time, processing time, and their impact on data freshness and correctness in real-time pipelines.
- Data Observability: How to Build a Modern Data Stack for Reliable Data, Eric Barr, Kevin Kandel, 2024 (O'Reilly Media) - A practical guide dedicated to building data observability platforms, directly addressing the monitoring of data quality attributes like freshness and latency in production data environments.
- Working with Time, The Apache Flink Community, 2024 - Official documentation explaining the distinctions between event time, ingestion time, and processing time within the context of stream processing, which are fundamental to accurately measuring and monitoring data freshness and latency.
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