## Logical entity

URL: https://docs.atlan.com/product/capabilities/data-models/concepts/what-is-a-logical-entity

> A logical entity is an implementation-agnostic business object in a data model, mapped to the assets that implement it in your data sources

A logical entity is a business object in a data model, such as `Customer`, `Order`, or `Loan`, defined at a level of detail that's independent of how or where the data is stored. Atlan records the abstraction level of every data model asset in a `type` attribute: conceptual, logical, or physical. A logical entity is an entity asset whose type is `logical`.

## Three levels of abstraction

Data models represent information at three levels of abstraction, and each level answers a different question about your data:

- **Conceptual**: The overall structure of the content without specific details. This is the most abstract form and the starting point for new data initiatives.
- **Logical**: An implementation-agnostic breakdown of data into specific objects and the interactions between those objects. This is the level of a logical entity.
- **Physical**: A refined adaptation of those data concepts conforming to a particular software application or data storage system, taking into account naming conventions, optimizations, partitioning, and more.

Atlan ingests data models as a hierarchy: a data model contains versions, a version contains entities, and an entity contains attributes. Entities can also carry associations that connect them to other entities.

## Why logical entities matter

Because a logical entity is implementation-agnostic, you can model the business meaning of your data once and keep that model stable while systems change underneath it. Business and technical teams can work from the same objects and interactions without tool boundaries, and the model provides a stable reference for impact analysis when a physical system changes.

## How logical entities relate to each other

Entities within a model relate to each other in two ways:

- **Association**: A peer-to-peer relationship between two entities, carrying a cardinality (one-to-one, one-to-many, many-to-one, or many-to-many) and an optionality that together define business rules. For example, a `Quote` provides pricing for many related `Orders` (one-to-many).
- **Generalization**: A parent-child relationship where a general entity captures the characteristics shared by specialized entities. For example, a `Loan` entity can generalize `Home Loan`, `Auto Loan`, and `Business Loan` entities.

For the full entity–relationship vocabulary, including attributes and ER diagrams, see [What are data models](https://docs.atlan.com/llms/governance/data-models/what-are-data-models/llms.txt).

## How logical entities connect to your assets

A logical entity on its own describes business meaning, not real data. Mapping bridges the two. Entities within a model can be mapped to entities in models of a different type, and entities can be mapped to the concrete assets in Atlan that implement them. For example, a logical entity `Order` can be mapped to an `Order` table in Snowflake.

These mappings keep the business model connected to your cataloged assets, so a change on either side can be assessed against the other.

## See also

- [What are data models](https://docs.atlan.com/llms/governance/data-models/what-are-data-models/llms.txt): The fundamentals of data modeling in Atlan, including the full ER vocabulary.
- [Data model ingestion](https://docs.atlan.com/llms/governance/data-models/references-data-model-ingestion/llms.txt): Ingest your ER models via an Excel template.
- [How to view data models](https://docs.atlan.com/llms/governance/data-models/view-data-models/llms.txt): Explore ingested data models in Atlan.

_Last updated: 13 November 2026._

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