ddia-systems
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distr
npx skills add wondelai/skills --skill ddia-systems --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 and frameworks come and go, but data persists for decades -- prioritize the long-term correctness, durability, and evolvability of the data layer. Most applications are data-intensive, not compute-intensive: the hard problems are data volume, complexity, and rate of change, and explicit consistency/availability/latency trade-offs separate robust systems from fragile ones. ## Scoring **Goal: 10/10.** Score a data architecture by the seven Quick Diagnostic rows below: award ~1.4 points per row answered "yes" with evidence (deliberate, documented trade-off), 0 where the answer is "no" or unknown. - **9-10:** every domain choice -- data model, storage engine, replication, partitioning, isolation, derived-data, fault handling -- is deliberate, documented, and matched to actual read/write/consistency requirements; failover tested. - **5-
- Core Principle
- Scoring
- The DDIA Framework
- 1. Data Models and Query Languages
- 2. Storage Engines
- 3. Replication
- 4. Partitioning
- 5. Transactions and Consistency
- 6. Batch and Stream Processing
- 7. Reliability and Fault Tolerance
- Common Mistakes
- Quick Diagnostic
- Further Reading
- About the Author
What does the ddia-systems skill do?
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distr
How do I install it?
Run `npx skills add wondelai/skills --skill ddia-systems --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wondelai/skills, a repository with 1,835 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.