ddia-systems

Evaluate data system architectures across replication, partitioning, and consistency models.

Updated Jul 31, 2026
One-click install
npx skills add https://github.com/fsolla/teqo --skill ddia-systems-fsolla
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ddia-systems
Source: https://github.com/fsolla/teqo/tree/main/.agents/skills/ddia-systems
Command: npx skills add https://github.com/fsolla/teqo --skill ddia-systems-fsolla

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexity of designing data-intensive applications by providing a structured framework for evaluating trade-offs in storage, consistency, and scalability.

Core Features & Use Cases

  • Architectural Diagnostics: Score your data system against seven core domains including replication, partitioning, and consistency models.
  • Decision Support: Navigate complex choices like SQL vs. NoSQL, LSM-trees vs. B-trees, and batch vs. stream processing.
  • Use Case: When designing a new microservice, use this Skill to determine if your consistency requirements necessitate a serializable transaction model or if eventual consistency is sufficient for your scale.

Quick Start

Use the ddia-systems skill to evaluate my current database architecture and identify potential consistency risks.

Frequently Asked Questions about ddia-systems

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate consistency risks in my current database architecture?

To evaluate consistency risks in your database architecture, score your data system against core domains like replication, partitioning, and consistency models to identify potential vulnerabilities and determine if serializable transactions or eventual consistency are required for your scale.

What is the best way to choose between SQL and NoSQL for distributed systems?

Choosing between SQL and NoSQL for distributed systems requires a rigorous trade-off analysis across storage engines, scalability requirements, and consistency models to determine which architecture best satisfies your specific data modeling needs.

How do I design scalable data-intensive applications with fault tolerance?

Designing scalable data-intensive applications with fault tolerance involves evaluating trade-offs in replication strategies and partitioning mechanisms to ensure your system architecture remains reliable and maintainable under distributed system constraints.

When do I need serializable transaction models instead of eventual consistency?

You need serializable transaction models instead of eventual consistency when your consistency requirements demand strict data integrity for operations, whereas eventual consistency is sufficient when prioritizing high scalability over immediate synchronization.

How does LSM-tree storage compare to B-trees for database design?

LSM-trees compared to B-trees for database design involves navigating complex storage engine choices where LSM-trees optimize write-heavy workloads while B-trees provide faster read operations, requiring trade-off analysis based on your specific access patterns.