Data Architecture
1. Overview
1.1. Data Architecture Definition
- Structured framework that dictates how data is collected, stored, managed, and utilized in a system.
1.2. Core Components
- Data Models: Abstract representations that define data elements and relationships.
- Data Flows: Pathways showing data movement between system components or processes.
- Data Storage: Mechanisms and technologies for data persistence (e.g., databases, data lakes).
- Data Integration: Combining data from different sources into a unified view (ETL/ELT processes).
- Data Governance: Policies and standards ensuring data quality, security, and privacy.
1.3. Architectural Patterns
- Traditional Data Warehouse: Centralized storage that supports decision-making; characterized by structured data.
- Data Lake: Storage system that handles raw and unstructured data; useful for big data analytics.
- Data Mesh: Decentralized architecture promoting domain-oriented data ownership and self-service analytics.
1.4. Evolution and Trends
- Movement towards cloud-based solutions and serverless architectures for scalability and flexibility.
- Increased importance on real-time data processing and analytics.
- Emphasis on automation via AI/ML for more efficient data handling.
1.5. Interrelated Disciplines
- Data Governance is crucial for maintaining data integrity across the architecture.
- Enterprise Architecture ensures alignment between data initiatives and business goals.
1.6. Functional Paritions
1.6.1. Operational Data Architecture
1.6.2. Technical Data Architecture
2. Principles of Good Data Architecture
2.1. Choose common components wisely
2.4. Architecture is leadership
2.5. Always be architecting
2.6. Build loosely coupled systems
2.7. Make reversible decisions