Book
Preface
I - Foundations of Data Reliability Engineering
1.
Foundations of Data Reliability Engineering
1.1.
Data Architecture
1.1.1.
Foundational Architectures
1.1.1.1.
Single-Tier Architecture
1.1.1.2.
Two-Tier Architecture
1.1.1.3.
Three-Tier Architecture
1.1.1.4.
N-Tier Architecture
1.1.2.
Modern Architectural Paradigms
1.1.2.1.
Microservices Architecture
1.1.2.2.
Service-Oriented Architecture (SOA)
1.1.2.3.
Cloud-Native Data Architectures
1.1.2.4.
Data Mesh
1.1.3.
Data Storage and Processing
1.1.3.1.
Data Lake Architecture
1.1.3.1.1.
Data Lake Layers
1.1.3.1.2.
Data Lake Zones
1.1.3.1.3.
Data Lake Maturity Stages
1.1.3.2.
Transactional Data Lakes
1.1.3.2.1.
Design Principles of Transactional Data Lakes
1.1.3.2.2.
Operational Use Cases
1.1.3.2.3.
Integrating Transactional Data Lakes in Data Ecosystems
1.1.3.3.
Data Warehouse Architecture
1.1.3.4.
Data Lakehouse Architecture
1.1.3.5.
Lambda Architecture
1.1.3.6.
Event-Driven Architecture (EDA)
1.1.4.
Data Integration and Access
1.1.4.1.
Data Virtualization
1.1.4.2.
Data Federation
1.1.4.3.
Interoperability and Data Standards
1.1.5.
Operational Data Management
1.1.5.1.
Operational Data Stores and OLTP Databases
1.1.6.
Data Governance and Management
1.1.6.1.
Introduction to Data Ethics and Privacy
1.1.6.2.
Data Governance and Quality
1.1.6.3.
Data Security and Privacy
1.1.6.4.
Compliance and Regulatory Considerations
1.1.7.
Components
1.1.7.1.
Data Repositories
1.1.7.2.
Data Sources
1.1.7.3.
Data Lake
1.1.7.4.
Data Warehouse
1.1.7.5.
Data Modelling
1.1.7.6.
Data Marts
1.1.7.7.
Data Lakehouse
1.1.7.8.
Slowly Changing Dimensions (SCD)
1.1.8.
Mixed Architectures
1.2.
Systems Reliability
1.2.1.
Understanding Reliability
1.2.1.1.
Introduction to Reliability
1.2.1.2.
Impediments
1.2.2.
Core Attributes of Reliable Systems
1.2.2.1.
Attributes
1.2.3.
Achieving Reliability
1.2.3.1.
Reliability Mechanisms Overview
1.2.3.1.1.
Fault Prevention
1.2.3.1.1.1.
Fault Prevention: Avoidance
1.2.3.1.1.2.
Fault Prevention: Elimination
1.2.3.1.2.
Fault Tolerance
1.2.3.1.3.
Fault Prediction
1.2.3.2.
Risk Management and Assessment in Data Systems
1.2.3.3.
Change Management in Data Systems
1.2.3.4.
Reliability Toolkit
1.2.3.4.1.
Observability
1.2.3.4.2.
Data Quality Automation
1.2.3.4.3.
Version Control
1.2.3.4.4.
Data Lineage
1.2.3.4.5.
Workflow Orchestration
1.2.3.4.6.
Data Transformation and Testing
1.2.3.4.7.
Infrastructure as Code (IaC)
1.2.3.4.8.
Container Orchestration
1.3.
Data Quality
1.3.1.
Foundations of Data Quality
1.3.2.
Master Data
1.3.3.
Data Management
1.3.4.
Data Quality Models
1.3.4.1.
Accuracy Dimension
1.3.4.2.
Completeness Dimension
1.3.4.3.
Consistency Dimension
1.3.4.4.
Timeliness Dimension
1.3.4.5.
Relevance Dimension
1.3.4.6.
Reliability Dimension
1.3.4.7.
Uniqueness Dimension
1.3.4.8.
Validity Dimension
1.3.4.9.
Accessibility Dimension
1.3.4.10.
Integrity Dimension
1.3.4.11.
Metrics/Audit Database & Service
1.3.4.12.
Final Thoughts on Data Quality Dimensions
1.3.5.
Final Thoughts on Data Quality
II - Data Reliability Engineering Toolkit: Tools & Methodologies
2.
Practical Methodologies and Tools
2.1.
Processes
2.1.1.
Introduction to Data Processes
2.1.2.
Data Ingestion and Integration
2.1.2.1.
Ingesting Internal Data
2.1.2.1.1.
Ingesting Operational Systems Data
2.1.2.1.2.
Ingesting Flat File Systems Data
2.1.2.1.3.
Incorporating CRM & ERP Data
2.1.2.2.
Handling 3rd Party Data
2.1.2.3.
Storing Data: Lakes vs. Warehouses
2.1.3.
Data Orchestration
2.1.3.1.
Understanding Data Orchestration
2.1.3.2.
Orchestration Tools and Platforms
2.1.3.2.1.
Apache Airflow: A Comprehensive Guide
2.1.3.3.
Best Practices for Data Orchestration
2.1.3.4.
Data Orchestration Strategies
2.1.4.
Data Pipelines
2.1.4.1.
Basics of Data Pipelines
2.1.4.2.
Designing Scalable Data Pipelines
2.1.4.3.
Monitoring Data Pipelines
2.1.4.3.1.
Pipeline Observability
2.1.4.3.2.
Metadata Management
2.1.4.4.
Pipeline Maturity Levels
2.1.4.4.1.
From Reactive to Proactive
2.1.4.4.2.
Implementing Dynamic Redundancies
2.1.4.4.3.
Towards Self-Healing Pipelines
2.1.4.4.4.
Advanced Error Recovery
2.1.4.5.
Data Transformation
2.1.4.5.1.
Transforming Data with dbt
2.1.4.6.
Integrating Data Quality within Processes
2.1.5.
ELT and ETL Processes
2.1.5.1.
Implementing ELT Processes
2.1.5.2.
Implementing ETL Processes
2.1.5.3.
ELT/ETL Tools and Technologies
2.1.6.
Tool Selection for Data Processing
2.1.6.1.
Identifying Requirements
2.1.6.2.
Version Control Integration
2.1.6.3.
Observability Integration
2.1.6.4.
Containerization and Deployment
2.1.6.5.
Security Considerations
2.1.7.
Operationalizing Data Workflows
2.1.7.1.
Workflow Automation and Scheduling
2.1.7.2.
Error Handling and Recovery in Data Workflows
2.1.7.3.
Workflow Adaptability and Resilience
2.1.7.4.
Handling Complex Dependencies in Workflow Execution
2.1.7.5.
System Diagnostics Design for Data Systems
2.1.8.
Process Optimization and Maintenance
2.1.8.1.
Optimizing Data Processes for Performance
2.1.8.2.
Maintaining and Updating Data Processes
2.1.8.3.
Future-Proofing Data Operations
2.1.8.4.
Data Backfilling Strategies
2.2.
Operational Excellence in Data Reliability
2.2.1.
Cross-Functional Collaboration
2.2.1.1.
DataOps for Streamlined Data Management
2.2.1.2.
Applying DevOps Principles to Data Systems
2.2.1.3.
Agile Methodologies in Data Projects
2.2.1.4.
Implementing CI/CD in Data Pipelines
2.2.1.5.
Site Reliability Engineering (SRE) Practices for Data
2.2.2.
Building a Data Reliability Framework
2.2.3.
Tools and Technologies for Ensuring Data Reliability
2.2.4.
Monitoring, Metrics, and SLAs in Data Systems
2.2.5.
Feedback Loops and Continuous Improvement
2.2.6.
Human Factors in Data Systems
2.2.6.1.
Human Factors, Human Interaction, Human Errors, and Latent Human Error
III - Advanced Applications and Real-World Case Studies
3.
Advanced Applications and Real-World Case Studies
3.1.
Advanced Topics in Data Reliability Engineering
3.1.1.
Processes
3.1.1.1.
Managing Dependencies in Data Pipelines
3.1.1.2.
Dynamic Scheduling of Tasks
3.1.1.3.
Advanced Data Integration Techniques
3.1.1.3.1.
Data Federation and Virtualization
3.1.1.3.2.
Streaming Data and Real-Time Processing
3.1.1.4.
Failure/Reliability Testing in Data Systems
3.1.2.
Operations
3.1.2.1.
Data System Scalability and Performance
3.1.2.2.
Security and Compliance in Data Operations
3.1.2.3.
Disaster Recovery and Business Continuity Planning
3.1.2.4.
Cost Optimization in Data Systems
3.2.
Emerging Trends in Data Reliability Engineering
3.3.
Challenges in Advanced Data Reliability Engineering
3.4.
Use Cases
3.4.1.
Opetence Inc.
3.4.2.
Aranduka Inc.
IV - Incorporating Data Reliability Engineering
4.
Incorporating Data Reliability Engineering
4.1.
Data Engineers
4.2.
Data Platform Engineers
4.3.
DevOps Engineers
4.4.
Solutions Architects
4.5.
Cloud Engineers
4.6.
Data Architects
4.7.
Analytics Engineers
4.8.
Data Scientists and Data Analysts
4.9.
BI Professionals
V - Appendices and Resources
5.
Appendices and Resources
5.1.
Extended Reliability Toolkit
5.1.1.
Corrective Actions
5.1.2.
Reliability Block Diagrams
5.1.3.
Chaos Engineering Tools
5.1.4.
High Availability
5.1.5.
Antifragility
5.1.6.
Bulkhead Pattern
5.1.7.
Cold Standby
5.1.8.
Single Point of Failure (SPOF)
5.1.9.
General Reliability Development Hazard Logs (GRDHL)
5.1.10.
Spare Parts Stocking Strategy
5.1.11.
Availability Controls
5.1.12.
Failure Mode and Effects Analysis (FMEA)
5.1.13.
Assessing Technology Maturity in Data Projects: An Adaptation of TRL
5.1.14.
Adapting DVP Principles for Data Systems
5.2.
Appendices
5.2.1.
Enterprise Service Bus (ESB)
Author
Objectives
Structure
Epilogue
Dictionary
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Back Cover
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Rust
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Ayu
Latte
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Data Reliability Engineering
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Operational Use Cases