Book Overflow "Building Evolutionary Architectures" by Neal Ford, Rebecca Parsons, Patrick Kua, and Pramod Sadalage (Part 2)
11 snips
Aug 26, 2024 Delve into the world of evolutionary architectures and their nuances in software design. Learn about the pitfalls of tight coupling and the importance of adaptability in data engineering. Discover the challenges posed by low-code solutions and the strategic decision-making required in agile development. The discussion emphasizes the need for a deep understanding of architectural anti-patterns, fostering a culture of awareness to avoid common mistakes, all while advocating for informed, iterative practices in tech.
AI Snips
Chapters
Books
Transcript
Episode notes
Data Engineering Lags
- Database schemas often lag behind other software engineering practices in terms of DevOps and agile methodologies.
- This can lead to tangled messes, over-reliance on advanced database features, and fear of schema changes.
Embrace Change
- Be comfortable changing internal structures, even if functionality stays the same.
- A good codebase with proper tests allows for bold refactoring and schema evolution.
Simplify Databases
- Minimize stored procedures and triggers in databases for better scalability and maintainability.
- Move complex logic to the application layer for easier testing and modification.
