Data Architecture and Engineering are crucial components of a data strategy, works in line with Data Strategy and consulting guidelines, focusing on designing, building, and maintaining the infrastructure and systems needed for efficient data management and analytics. Here’s a detailed look at both:
Data Architecture involves the design and structure of data systems to ensure they meet business requirements.
Key Components of Data Architecture
- Data Models:
- Conceptual Data Model: High-level view of organizational data and its relationships.
- Logical Data Model: Detailed map of entities, attributes, and relationships without considering physical implementation.
- Physical Data Model: Blueprint for actual database design, including tables, columns, and keys.
- Data Storage Solutions:
- Databases: Relational (SQL) and Non-relational (NoSQL) databases.
- Data Warehouses: Centralized repositories for structured data, optimized for query and analysis.
- Data Lakes: Storage repositories that hold large amounts of raw data in its native format.
- Data Integration:
- ETL (Extract, Transform, Load): Processes for extracting data from sources, transforming it to fit operational needs, and loading it into a destination.
- Data Pipelines: Automated workflows for moving and processing data between systems.
- Data Governance and Security:
- Data Policies: Rules and standards for data management.
- Access Control: Mechanisms to ensure only authorized users can access specific data.
- Data Quality Management: Processes for ensuring data accuracy, completeness, and consistency.
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