Data Architecture - Key Artifacts

 Referred Link - https://www.linkedin.com/posts/mr-deepak-bhardwaj_dataarchitecture-dataengineering-bigdata-activity-7218596032052363264-5jqN/

Data Architecture: Every Data Architect Must Know These Artifacts

Jumping into the world of data architecture can feel like diving into a vast ocean. To help you navigate, there are some essential tools and artefacts you need to master. These aren’t just technical jargon—they’re the bedrock of your data architecture journey.

Here’s a breakdown of what you need to know to kickstart your journey and make a real impact on your projects.

🔘 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬

↳ High-Level Architecture Diagrams: Visual overview of your data infrastructure, simplifying complexity.
↳ Architecture Blueprints: Data warehousing, data lakes, real-time analytics, and other common data architectures.
↳ Architecture Principles: Foundational guidelines and best practices for solid data solutions.

🔘 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧

↳ Detailed Design Documents: Comprehensive solutions covering components, interactions, and data flow.
↳ Integration Catalog: List of internal and external data producers and consumers.

🔘 𝐃𝐚𝐭𝐚 𝐅𝐥𝐨𝐰𝐬

↳ Data Flow Diagrams (DFDs): Illustrate data movement within your system, from sources to destinations.
↳ Process Flow Diagrams: Map steps and transformations data undergoes in the system.
↳ Pipeline Designs: Specifications for ETL/ELT processes and data pipelines.

🔘 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐑𝐞𝐜𝐨𝐫𝐝𝐬

↳ Architecture Decision Records (ADRs): Document architectural decisions, context, alternatives, and rationale.
↳ Technical Debt Register: Log known technical debt items, their impact, and resolution strategies.

🔘 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐬

↳ Conceptual Data Model: High-level representation of main entities and their relationships.
↳ Logical Data Model: Abstract entities, attributes, and relationships from physical details.
↳ Physical Data Model: Database implementation details, including tables, columns, and constraints.

🔘 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬

↳ Reusable Design Patterns: Standardized solutions for common data challenges like partitioning and caching.
↳ Pattern Application Guides: Guidance on when and how to apply specific design patterns.

🔘 𝐃𝐚𝐭𝐚 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬

↳ Data Naming Conventions: Standard rules for naming data elements to ensure consistency and clarity.
↳ Data Quality Standards: Criteria and metrics for maintaining high data quality.

Tags:

#Data Analytics, #Data Science, #Data Visualization, #Artifacts