ddia-systems

# Designing Data-Intensive Applications Framework A principled approach to building reliable, scalable, and maintainable data systems. Apply these principles when choosing databases, designing schemas, architecting distributed systems, or reasoning about consistency and fault tolerance. ## Core Principle **Data outlives code.** Applications are rewritten, languages change, frameworks come and go -- but data and its structure persist for decades. Every architectural decision must prioritize the long-term correctness, durability, and evolvability of the data layer above all else. **The foundation:** Most applications are data-intensive, not compute-intensive. The hard problems are the amount of data, its complexity, and the speed at which it changes. Understanding the trade-offs between consistency, availability, partition tolerance, latency, and throughput is what separates robust systems from fragile ones. ## Scoring **Goal: 10/10.** When reviewing or designing data architectures, rate them 0-10 based on adherence to the principles below. A 10/10 means deliberate trade-off choices for data models, storage engines, replication, partitioning, transactions, and processing pipelines; lower scores indicate accidental complexity or ignored failure modes. Always provide the current score and specific improvements needed to reach 10/10. ## The DDIA Framework Seven domains for reasoning about data-intensive systems: ### 1. Data Models and Query Languages **Core concept:** Th...