Data-Intensive Apps
Designing reliable, scalable data systems
Design reliable, scalable, and maintainable data systems using Martin Kleppmann's comprehensive guide. This skill equips your AI agent with deep knowledge of storage engines, replication, partitioning, consistency models, and stream processing for building data-intensive applications.
Designing Data-Intensive Applications
npx skills add wondelai/skills/ddia-systems What your agent learns
Storage Engines
Understand the trade-offs between B-trees and LSM-trees, row vs columnar storage, and when to use each.
Replication & Partitioning
Choose between single-leader, multi-leader, and leaderless replication. Partition data by key range or hash for scalability.
Consistency & Consensus
Navigate linearizability, causal consistency, eventual consistency, and distributed consensus algorithms like Raft and Paxos.
Batch & Stream Processing
Design data pipelines using MapReduce, dataflow engines, and stream processing with exactly-once semantics.
Distributed System Failures
Design for unreliable networks, clocks, and nodes. Understand the impossibility results and practical workarounds.
Try these with the skill installed
Recommend a storage engine and replication strategy for our write-heavy workload using ddia-systems skill
Database designDesign a data partitioning scheme that avoids hotspots using ddia-systems skill
ScalabilityEvaluate consistency trade-offs for our distributed checkout system using ddia-systems skill
System designDesign a stream processing pipeline for real-time analytics using ddia-systems skill
Data architectureInstall Data-Intensive Apps
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npx skills add wondelai/skills/ddia-systems MIT Licensed · Works with Claude Code, Cursor, Claude Cowork & OpenClaw · No account needed