data-architecture

Design data movement and transformation pipelines. Show how data flows between systems, transforms, and where it's stored. Use when architecting data integrations or ETL processes.

Establish data ownership, quality standards, compliance policies, and metadata management. Build organizational data practices. Use when defining data strategy or improving data quality and compliance.

Design data models for business domains using entity-relationship or domain-driven design. Use when modeling new domains or refactoring data structures.

Design batch and streaming data pipelines. Plan ingestion, transformation, quality checks, and failure recovery. Use when building ETL/ELT systems or data infrastructure.

Store state as immutable event log instead of current values. Build auditable, event-driven systems with full history. Use when auditability, temporal queries, or event-driven processing matters.

Integrate data across systems using ETL, CDC, event streaming, and API patterns. Design robust, maintainable data integrations. Use when connecting disparate data sources or microservices.

Manage database schema changes while maintaining backwards compatibility. Handle migrations, versioning, and zero-downtime deployments. Use when evolving data models in production systems.

Choose the right database technology for specific workloads. Evaluate relational, NoSQL, data warehouses, and search engines. Use when selecting storage systems for new features or optimizing existing ones.