Course Outline: Modern Data Architectures

Section 1: Onboarding Patterns and Landing Zones [1 hour]

• Overview of Onboarding Patterns:
  Understanding different onboarding strategies
  Importance of a well-defined landing zone

• Landing Zone Dynamics:
  Characteristics and components of landing zones
  Best practices for implementing landing zones

• Foundations of Streaming Data:
  Key concepts in streaming data architectures
  Differences between batch and stream processing

Section 2: Layered Approaches in Data Architecture [1.5 hours]

• Introduction to Layered Architectures:
  Importance of layered data processing
  Common patterns and their applications

• Raw/Business Vault and Info Marts:
  Understanding the layers: raw, business vault, info mart
  How they interact and support data integrity

• Staging/Intermediate/Presentation Layers:
  Detailed look into data flow across layers
  Enhancing data readiness and availability

• Medallion Architecture:
  Analyzing the medallion approach
  Comparison with other leading methodologies

Section 3: Exploring Data Marts [1 hour]

• Kimball vs. Inmon Approaches:
  Key differences and implementation scenarios

• Star and Snowflake Schema:
  Understanding schema design and application
  Advantages and limitations of each schema

• Designing Fact Tables and Dimensions:
  Types of fact tables: transactional, snapshot, accumulating
  Effective usage of dimensions for data navigation

• Utilizing Reports and Dashboards:
  Aligning data marts with reporting tools

Section 4: Intermediate Layer and Metadata Management [0.5 hour]

• Capturing Business Logic:
  Methods to incorporate business logic in data layers
  Importance of a business glossary

• Managing Metadata:
  Role of metadata in modern architectures
  Techniques for effective metadata management

• Organizational Vernacular:
  Creating and maintaining an organizational data language

Section 5: Final Review

• Review of Key Concepts:
  Summarization of onboarding patterns, layered approaches, and data marts

• Future Learning Directions:
  Suggested areas for further study and exploration in data architecture