data-systems-architecture
Data Systems Architecture
Overview
Core principle: Good data system architecture balances reliability (correct operation under faults), scalability (handling growth gracefully), and maintainability (enabling productive change over time). Every architectural decision involves trade-offs between these concerns.
This skill synthesizes knowledge from three foundational texts:
- Designing Data-Intensive Applications (Kleppmann) - distributed systems, storage engines, scaling
- The Art of PostgreSQL (Fontaine) - PostgreSQL-specific patterns, SQL as programming
- PostgreSQL Query Optimization (Dombrovskaya et al.) - execution plans, performance tuning
When to Use
| Symptom | Start With |
|---|---|
| Designing a new database/schema | 01-foundational-principles.md |
| Normalization vs denormalization decisions | 02-data-modeling.md |
| Need to understand OLTP vs OLAP | 03-storage-engines.md |
| Slow queries, index selection | 04-indexing.md |
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