Visual data modeling

Define tables, columns, relationships, keys, sequences, indexes, domains, views, procedures and triggers

IDEF1X, Crow's Foot, Codasyl and Relational notations

Auto-arrange tables in the diagram

Advanced keys management

Reflexive relationships

Multiple diagrams

Multiple projects management

Syntax highlighting

Diagram sizing

12 A0

12 A0

12 A0

Split and merge relationship

Supported databases

Oracle database

Micosoft SQL Server

MySQL

MariaDB

PostgreSQL

SQLite

Firebird

Microsoft Azure SQL database

Azure Synapse Dedicated SQL Pool

Amazon Redshift

Amazon RDS

Forward/Reverse engineering

Ability to manage connections

SSL Secure connections (PostgreSQL and MySQL)

SSH connection to a remote server using password authentication and public key authentication method

Windows authentication method in MS SQL Server connection

Model validation

Generation of SQL/DDL script from data models

Generation of databases from data models

Support for Before and After scripts at both project and database table levels

Import from local and remote databases(Reverse engineering)

Version management

Reporting

Export model as an image

Data model printing

Model documentation settings

Generating navigable model documentation in HTML format

Generating model documentation in MS Word format

Generating model documentation directly into Confluence

Test data

Validate a model using a database sandbox

Data forms

Data grids

Test data generation

Generate maximum 10 000 rows per table

Generate unlimited rows per table

Generative AI features ⓘ

Automatically generate entity relationship diagrams (ERDs) from natural language inputs such as data model descriptions, user stories, or requirements, using the generative AI feature

Use the generative AI feature to update an existing data model by auto-generating tables & relationships from descriptions, user stories, or requirements

Advanced Metadata Query Assistant powered by ChatGPT API: Ask and get answers as reports and statistics from your data model

Expert mode into the Advanced Metadata Query Assistant: See the SQL query extracted from the natural language question and the ability to fine-tune and edit the query as needed

AI-Powered description generation for Procedures, Views, and Triggers

Advanced tools

Collaborative modeling with Git: Configure ERBuilder data modeler to work with Git. Browse different versions of the model from the repository, merge and compare with the local model

Advanced exploration of a data model

Advanced search in the data model browser

Generate web user interface for CRUD applications

Switch to another target database

Compare model to model

Compare model to database

Compare database to database

Generate compare HTML report

Generate database/model synchronization script

Update model from database

Build an enterprise data dictionary automatically

Generate data dictionary/documentation report using the ERBuilder command line

Requirements management: Ability to create and assign requirements to tables, columns, constraints, triggers, procedures and relationships

Document a database with user-defined metadata fields

Other features

Ability to find objects by name

Global search and replace

Replacing data type

Treeview contextual menu

Copy/Paste

Undo/Redo

Options management

Reach objects from treeview