data-architecture

Define data boundaries, consistency rules, and migration strategies for multi-service systems.

Updated Feb 4, 2026

Search Tags:#consistency #caching #migrations #search #bounded-contexts #data-architecture

Ready to run

npx skills add https://github.com/Tirael/cursor-template --skill data-architecture-tirael

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-architecture
Source: https://github.com/Tirael/cursor-template/tree/main/.cursor/skills/data-architecture
Command: npx skills add https://github.com/Tirael/cursor-template --skill data-architecture-tirael

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Defines data boundaries across bounded contexts, ensures data consistency, and standardizes migrations, caching, and search strategies to prevent data silos.

Core Features & Use Cases

Bounded-context data governance with clear ownership and API/contracts.
Versioned migrations and automatic deployment of schema changes.
Cache-aside strategy with event-driven invalidation.
Separate search index and asynchronous reindexing for scalable discovery.

Quick Start

Prototype a data-architecture plan by outlining bounded contexts, selecting a consistency model, and documenting migration and caching rules.

Frequently Asked Questions about data-architecture

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

FAQPage Schema

How do I define data boundaries for multi-service microservices architectures?▼

Data boundaries for multi-service systems are defined by establishing per-service ownership, contract-based data exposure, and clear bounded contexts to prevent data silos. This enforces strict separation where each service owns its data and exposes it only through APIs or contracts.

What is the best way to handle schema migrations across bounded contexts in domain-driven design?▼

Schema migrations across bounded contexts are handled using versioned migrations with automatic deployment of schema changes. This standardizes schema evolution across teams, ensuring that domain-driven design projects maintain data consistency without manual synchronization errors.

How does cache-aside pattern work with event-driven invalidation in distributed systems?▼

The cache-aside pattern with event-driven invalidation works by loading data into the cache on demand and using events to invalidate stale entries automatically. This maintains data consistency across multi-service systems by ensuring cached data is updated whenever the underlying source changes.

Can I use a separate search index for scalable discovery in event-driven architectures?▼

Yes, a separate search index is used for scalable discovery in event-driven architectures by implementing asynchronous reindexing. This decouples search requirements from transactional data stores, allowing teams to query optimized search indexes without overloading primary data boundaries.

Does this approach require specific consistency models for microservices data ownership?▼

Yes, defining microservices data ownership requires selecting a specific consistency model tailored to your bounded contexts. You prototype a data-architecture plan by outlining these contexts, choosing the appropriate consistency rules, and documenting migration and caching strategies accordingly.

When should I not use contract-based data exposure for domain-driven design projects?▼

Contract-based data exposure may be unnecessary for simple, single-service applications that do not require strict bounded contexts or multi-service coordination. If your system does not face data silo risks or cross-team schema evolution conflicts, lighter data sharing approaches might suffice.