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
The Pillars of Data Observability
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- Site Reliability Engineering: How Google Runs Production Systems, Betsy Beyer, Chris Jones, Jennifer Petoff, and Niall Richard Murphy, 2016 (O'Reilly Media) - A foundational guide establishing the principles of Site Reliability Engineering, including the use of metrics, logs, and traces for system observability.
- Data Downtime: The Most Impactful Data Quality Incidents and How to Avoid Them, Barr Moses, Lior Gavish, Jeremy Levy, 2022 (Wiley) DOI: 10.1002/9781119864259 - Defines data downtime and introduces data observability as a solution, covering freshness, volume, and schema as key dimensions for data health monitoring.
- Data Lineage for Big Data: A Survey, Kun Ma, Yongfeng Huang, and Guoping Long, 2019 Journal of Parallel and Distributed Computing, Vol. 129 (Elsevier) DOI: 10.1016/j.jpdc.2019.03.003 - A comprehensive academic survey of data lineage concepts, techniques, and challenges in big data environments, detailing its role in understanding data flow.
- The Data Engineering Cookbook, Andreas Kretz, 2019 (The Data Engineering Academy) - Offers practical approaches to building robust data systems, including sections on monitoring, alerting, and ensuring data quality in production.
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