Aaru Features

Provides support for conceptual, logical and physical layers of modeling with transitions between them.

Defines relationships (e.g., foreign keys, associations) and constraints (e.g., uniqueness, referential integrity) between model elements.

Defines entities and their attributes, including identifiers and key constraints.

Generates implementation‑ready artifacts such as SQL DDL, platform‑specific scripts, JSON Schema, DBML, Avro, etc.

Map model datatypes and structures to the target database/warehouse/lakehouse platform types and structures.

Provides model‑vs‑schema comparison and reconciliation so model and implementation can stay aligned.

Imports existing schemas or connect to live systems to reverse‑engineer models from the current database or data‑warehouse.

Enforces naming conventions, modelling standards, reuse of domains/objects, versioning of standard templates.

Validates models for datatype correctness, broken references, invalid nested structures (e.g., JSON, NoSQL), and ensure integrity before deployment.

Support collaboration features: roles/permissions, comments/annotations, compare/merge models, version control compatibility.

Publishs visual diagrams and export documentation (HTML, PDF, Markdown) and provide read‑only stakeholder views.

Provides analysis of downstream impacts of model changes (e.g., what tables/columns will be affected) and version history/reporting.

Provide specialized, emerging, or cross-functional capabilities that enhance processes, workflows, or decision-making

Have features and use cases that do not fit into existing AI software categories

Address unique or niche requirements not covered by mainstream software solutions