# Data Architecture

> Conceptual, logical and physical data models, data modelling practice, data storage and data flow concerns.

## Concepts

- [Command Query Responsibility Segregation](https://arc.fuet.ch/concepts/data-architecture/command-query-responsibility-segregation.md): CQRS splits a system into a write side (commands) and a read side (queries), each with its own model. Teams can tune and scale each side separately, at the cost of extra complexity and possibly stale reads.
- [Conceptual Data Model](https://arc.fuet.ch/concepts/data-architecture/conceptual-data-model.md): A conceptual data model is a high-level description of the business information an organization needs. It leaves out database details. Logical modeling later turns it into a structured database design.
- [Conceptual data model building method](https://arc.fuet.ch/concepts/data-architecture/conceptual-data-model-building-method.md): A conceptual data model is built step by step: understand the business, find entities, add attributes and relationships, validate with stakeholders, then document. It describes what data is needed, not how to store it.
- [Conceptual Data Model Preparation for Logical and Physical Models](https://arc.fuet.ch/concepts/data-architecture/conceptual-data-model-preparation-for-logical-and-physical-models.md): Preparing a conceptual data model means checking that it covers the business needs and defines its entities, attributes and relationships clearly. The checked model then becomes the basis for the logical and physical models.
- [Conceptual, Logical and Physical Data Model](https://arc.fuet.ch/concepts/data-architecture/conceptual-vs-logical-vs-physical-data-model.md): Data models come in three levels. The conceptual level describes the business domain. The logical level shapes it for a chosen kind of data technology. The physical level describes how one specific database stores the data.
- [Data Architecture](https://arc.fuet.ch/concepts/data-architecture/data-architecture.md): Data architecture describes how an organization's data is sourced, stored, managed, integrated and protected so it supports business goals. It gives business and technical teams one shared view of data repositories, relationships and ownership.
- [Data Architecture Diagram Type](https://arc.fuet.ch/concepts/data-architecture/data-architecture-diagram-types.md): Data diagrams work at different levels of detail. A conceptual diagram names core business entities. A logical diagram adds keys and attributes. A physical model fits one specific database.
- [Data reusability and standardized access](https://arc.fuet.ch/concepts/data-architecture/data-reusability-and-standardized-access.md): Systems should expose their data in a shared, standardized form so other teams can reuse it without heavy cleanup. Common tools are data products, agreed identifiers, and a canonical data model.
- [Define Target Data Architecture Method Phase](https://arc.fuet.ch/concepts/data-architecture/define-target-data-architecture-method-phase.md): This phase defines the major data types and data sources a business needs. It works at the level of data entities, not storage design. Its results feed matrices, gap analysis and arc42 documentation.
- [Logical Data Model](https://arc.fuet.ch/concepts/data-architecture/logical-data-model.md): A logical data model defines the structure of business data (entities, keys, relationships) without naming any database engine. It sits between the conceptual model and the physical model.
- [Logical vs. physical data model](https://arc.fuet.ch/concepts/data-architecture/logical-vs-physical-data-model.md): A logical data model describes business data without tying it to any database. A physical data model refines it for one specific database, adding names, data types, keys and indexes. One logical model can yield several physical designs.
- [Physical Data Model](https://arc.fuet.ch/concepts/data-architecture/physical-data-model.md): A physical data model describes how data is stored in one specific database product. It defines keys, indexes, and storage settings, and it may bend the logical design for speed.
- [Publishing the conceptual data model](https://arc.fuet.ch/concepts/data-architecture/publishing-the-conceptual-data-model.md): Publishing the conceptual data model makes it available to everyone as a shared glossary. Someone must own it, and it must change as the team's understanding of the domain grows.
- [Use of external and open data](https://arc.fuet.ch/concepts/data-architecture/use-of-external-and-open-data.md): External data, especially open data, is a useful input for applications. Open data is public domain or openly licensed, free to access, and machine-readable. Architects should plan for it early.