[book] Restructure Data Architecture chapters

[jeffersonroth]

Restructure Data Architecture chapters:

• I - Foundations of Data Reliability Engineering
  • Data Reliability Engineering Principles
  • Data Architecture
  • Data Architecture Principles
  • Foundational Architectures ✓
    • Single-Tier Architecture ✓
    • Two-Tier Architecture ✓
    • Three-Tier Architecture ✓
    • N-Tier Architecture ✓
  • Modern Architectural Paradigms
    • Microservices Architecture ✓
    • Service-Oriented Architecture (SOA) ✓
    • Cloud-Native Data Architectures ✓
    • Data Mesh
  • Data Storage and Processing
    • Data Lake Architecture
    • Data Warehouse Architecture
    • Data Lakehouse Architecture
    • Lambda Architecture
    • Event-Driven Architecture (EDA)
  • Data Integration and Access
    • Data Virtualization
    • Data Federation
    • Interoperability and Data Standards
  • Operational Data Management
    • Operational Data Stores vs. Data Operational Stores
  • Data Governance and Management
    • Introduction to Data Ethics and Privacy
    • Data Governance and Quality
    • Data Security and Privacy
    • Compliance and Regulatory Considerations
  • Components
    • Data Repositories
    • Data Sources
    • Data Lake
    • Data Warehouse
    • Data Modelling
    • Data Marts
    • Data Lakehouse
    • Slowly Changing Dimensions (SCD)
  • Mixed Architectures
• II - Practical Methodologies and Tools
  • Processes(./concepts/processes.md)
  • Introduction to Data Processes
  • Data Ingestion and Integration
    • Ingesting Internal Data
    • Ingesting Operational Systems Data
    • Ingesting Flat File Systems Data
    • Incorporating CRM & ERP Data
    • Handling 3rd Party Data
    • Storing Data: Lakes vs. Warehouses
  • Data Orchestration
    • Understanding Data Orchestration
    • Orchestration Tools and Platforms
    • Apache Airflow: A Comprehensive Guide
    • Best Practices for Data Orchestration
    • Data Orchestration Strategies
  • Data Pipelines
    • Basics of Data Pipelines
    • Designing Scalable Data Pipelines
    • Monitoring Data Pipelines
    • Pipeline Observability
    • Metadata Management
    • Pipeline Maturity Levels
    • From Reactive to Proactive
    • Implementing Dynamic Redundancies
    • Towards Self-Healing Pipelines
    • Advanced Error Recovery
    • Data Transformation
    • Transforming Data with dbt
    • Integrating Data Quality within Processes
  • ELT and ETL Processes
    • Implementing ELT Processes
    • Implementing ETL Processes
    • ELT/ETL Tools and Technologies
  • Tool Selection for Data Processing
    • Identifying Requirements
    • Version Control Integration
    • Observability Integration
    • Containerization and Deployment
    • Security Considerations
  • Operationalizing Data Workflows
    • Workflow Automation and Scheduling
    • Error Handling and Recovery in Data Workflows
    • Workflow Adaptability and Resilience
    • Handling Complex Dependencies in Workflow Execution
    • System Diagnostics Design for Data Systems
  • Process Optimization and Maintenance
    • Optimizing Data Processes for Performance
    • Maintaining and Updating Data Processes
    • Future-Proofing Data Operations
    • Data Backfilling Strategies
  • Operational Excellence in Data Reliability
  • Cross-Functional Collaboration
    • DataOps for Streamlined Data Management
    • Applying DevOps Principles to Data Systems
    • Agile Methodologies in Data Projects
    • Implementing CI/CD in Data Pipelines
    • Site Reliability Engineering (SRE) Practices for Data
  • Building a Data Reliability Framework
  • Tools and Technologies for Ensuring Data Reliability
  • Monitoring, Metrics, and SLAs in Data Systems
  • Feedback Loops and Continuous Improvement
  • Human Factors in Data Systems
    • Human Factors, Human Interaction, Human Errors, and Latent Human Error