Quest Data Modeler Release Notes

30 September 2026

Okta SAML SSO Support

Authenticate to Quest Data Modeler using Okta single sign-on (SSO)

You must be assigned the QDM user role in QTDMP for your organization. For more information, refer to the Assigning a role to a user topic.

Oracle Support

Build and maintain data models for Oracle 26ai databases
Use Oracle 26ai databases with model comparison, model derivation, forward engineering, and reverse engineering features

Expanded Git Provider Support

Connect a model to Azure DevOps, GitLab, or Bitbucket, in addition to the existing GitHub
Authenticate to all supported providers using a personal access token (PAT)

Git support now extends beyond GitHub to Azure DevOps, GitLab, and Bitbucket, with the same branching, commit, and pull request workflow across all supported providers.

QDM Integration with Quest erwin Mart Portal (on-premises)

Connect Quest Data Modeler to Quest erwin Mart Portal (on-premises)
Provide the Mart instance URL and port number, which must use a valid SSL certificate
Ensure that Mart users are configured for single sign-on (SSO) to access QDM

Data Movement Sources

Capture where your physical model's data comes from, directly in the model
Import source tables and columns from a File, Database, CSV, or the Mart
Map model columns to their sources and trace each one through vertical lineage

Turn scattered source knowledge into a single, governed source-to-target reference.

AI Copilot Conversation Enhancements

Send a new message while the AI Copilot is still processing a request, and choose how to handle it
Add to queue to run your message automatically after the current task finishes
Stop and send to replace the work in progress and start the new message right away
Steer with message to refine or extend the current task without restarting it

Derive Models Using AI

Derive a new model from the open model using a plain-language prompt in the AI Copilot Conversation pane, without using the Derive Model wizard
Specify the target model type and database in the prompt, for example, a physical Snowflake model derived from a logical model
Create the derived model as a separate model, leaving the source model unchanged and linked to it through vertical lineage

Model Lineage Using AI

Trace the vertical lineage of model objects from the AI Copilot Conversation pane and view the results as a text summary
View the target column, upstream data movement source, source column, and lineage type, such as DS Mapping, for columns mapped to data movement sources
Trace data or column lineage within the model by specifying the lineage type in the prompt

Model-Level Reports

Create reports for a specific model, in addition to the existing Mart reports
Search for or select a model, including the models that are open in the modeler
Select Logical, Physical, or Logical/Physical as the report type, based on the model type
Report on the subjects and properties available for the selected model and report type, including database-specific objects in physical and logical/physical models
Apply filters to refine the results

Model-level reports use the same run, view, and export options as Mart reports. Mart reports are unchanged.

Incremental Save for Mart Models

Save only the changes made since the previous save instead of rewriting the entire model each time
Use Incremental Save when saving models to Mart manually or through AI Copilot
Track updates as new model versions in Mart to support efficient save operations and version tracking

Google BigQuery RECORD/STRUCT Support

Set a table column's physical data type to RECORD/STRUCT
Use the Add field option to add nested fields to a RECORD/STRUCT column
Define up to 16 levels of nested fields under a RECORD/STRUCT column

31 August 2026

Export Model Ontology

Export models as RDF/TTL Ontology Files, capturing your model's business ontology as classes and datatype properties
Export models as RDF/TTL Constraint Files to generate shape-style validation rules for pre-deployment testing
Reuse a consistent, shared ontology vocabulary across models by exporting entities and attributes with matching class and property names
Integrate exported RDF/TTL with knowledge graphs, semantic tools, governance platforms, and AI agents using an open, standards-based format

This capability adds business meaning to your existing data models so that AI agents and semantic tooling can consume model semantics directly, without proprietary connectors.

Google BigQuery Support

Create and manage Google BigQuery data models, compare versions, and generate database scripts
Supports Forward Engineering, Reverse Engineering, Complete Compare, and Alter Script generation
Connect using either a Google Cloud service account key file or OAuth 2.0 authentication for secure, sign-in-based access
To use OAuth authentication, complete the one-time client setup

For more information, refer to the Configure OAuth for Google BigQuery topic.

AI Model Generator Enhancements

Define custom AI instructions once (AI Personalization) and apply them automatically across models, including modeling standards such as naming conventions and database-specific rules
Upload images in SVG, JPG/JPEG, and PNG formats to generate models from diagrams or schemas
Use built-in, industry-standard prompts to produce models that follow established modeling patterns, without writing detailed instructions from scratch
Use these inputs as a quick starting point, alongside text-based prompts

Snowflake MFA Support

Connect to Snowflake using multi-factor authentication (MFA) for stronger, more secure connections
Supports the additional verification step during sign-in
Helps you meet organizational security and compliance requirements

PostgreSQL Owner Support

Model, generate, and reverse engineer PostgreSQL object owners to maintain consistent ownership information between the model and the database

Vertical Lineage

View lineage events, including reverse engineering from a script file or database, derivation from another model, and import history from earlier versions of the modeling application
Trace the evolution of model objects, including columns, tables, views, and materialized views, over time, alongside the existing horizontal upstream and downstream lineage view

Git Integration

Connect a model directly to a GitHub repository and manage version control without leaving the modeling workspace
Create and switch branches, commit and push changes, and sync with the remote using fetch, pull, and push
Create pull requests and merge branches while adhering to the approval rules configured in GitHub
Run pre-deployment validation and monitor the read-only pipeline status before deployment
Manage credentials, repository connections, and the audit log from the centralized Git configuration settings
Import a specific version of a DDL file into Quest Data Modeler from any branch, tag, or commit

Reports

Create, run, and export reports for the models stored in your Quest erwin Mart Portal
Base reports on a Cross Model Report subject to query design content across models, or a Mart Administrative Report subject to query Mart administration and usage information
Select the properties to include and apply filters, including run-time filters such as specifying the model name when the report runs
Group, sort, and adjust how results are displayed without changing the saved report definition
Export reports as delimited text, HTML, or PDF, with PDF-specific options for configurable headers, footers, a table of contents, an index, paper size, and orientation
Save, share, or delete reports. Access to reports is governed by your Mart permissions.

Search Entities

Every Global Search is now a semantic search
Results are automatically relevance-ranked, surfacing exact matches, semantically similar entities, and related business terms together
Find the right entities and business terms even when naming conventions differ across teams or domains, with no additional setup required

Subject Area Explorer

A new Subject Area Explorer tab is added next to the Model Explorer tab in the Modeler page
View the entities, tables, and views included as members of the currently selected subject area without opening the Subject Area Quick Editor
Quickly switch context between the full model structure and a focused, business-domain view of its members

30 July 2026

Model Lineage

Visualize how data moves and connects across entities, attributes, tables, columns, views, and materialized views within a model
Trace lineage upstream to identify source dependencies, or downstream to identify dependent objects
View lineage at both the table level and the column level directly on the diagram
Supported across conceptual, logical, and physical models, with entity-level lineage available for conceptual models

These capabilities help assess the impact of change, trace data quality issues to their source, and support governance, compliance, and audit requirements.

Mart Integration

Connect directly to Quest erwin Mart Portal, making your Quest erwin Mart Portal repository a trusted source of truth as you model
Search Quest erwin Mart Portal content, import entities with their complete attribute definitions
Let the AI modeling agent reference Quest erwin Mart Portal automatically when generating new models
Mart integration works in two ways in QDM:
- Global Search and Import: Search Mart from anywhere in DM Cloud and import entities or glossary terms directly into your current model
- AI Agent and Mart: Have the AI modeling agent automatically discover and reuse matching Mart entities when it generates a model from a prompt

PostgreSQL 16.0 Support

Build and maintain data models for PostgreSQL 16.0 databases
Use PostgreSQL 16 databases with model comparison, model derivation, forward engineering, and reverse engineering features

DBT Support

Export data model metadata as DBT-compatible YAML files directly from mart models
Choose exactly which user-defined properties (UDPs) are included in the exported YAML script using a UDP selection schema (XML). You can now filter and include UDPs:
- By object level: Model, Entity, and Attribute UDPs, which map to the source_metadata, table_metadata, and column_metadata sections of the output
- By scope: All logical UDPs, all physical UDPs, or specific UDPs by name (including scope-qualified names such as logical:pii_flag)
- With clear precedence: Exclude rules override include rules, and any object level you don't filter keeps all of its UDPs
  
  If no schema is attached, all UDPs are exported making existing integrations fully backward compatible. The result is cleaner, business-relevant YAML metadata with fewer unexpected keys in downstream DBT projects

Undo/Redo

Step backward and forward through model edits to quickly revert or reapply changes
Use keyboard shortcuts for undo and redo in the Quick Editor to modify and update model and object properties

These controls make it easier to experiment with changes and recover from mistakes without rebuilding the model.

Recent Model Deletion

Delete models directly from the Recent Models list to keep your workspace clean and organized

Minimap and Colour Legend

Use a navigable diagram mini map to move quickly around large models
Refer to the color legend to understand object color coding at a glance

App Version and What's New

View the application version and pages describing what's new and what's coming next from the Info option in the side menu

Databricks OAuth Support

Connect to Databricks using OAuth authentication
Authenticate through Databricks OAuth for secure, standards-based sign-in
Generate an OAuth access token per connection to securely access your Databricks workspace

Word/PDF Document Support

Generate data models directly from Word (.doc and .docx) and PDF documents
Extract and analyze information from both document text and embedded images

Save Models to Mart via AI Prompt

Save AI-generated or AI-refined models directly to the Mart using natural language prompts
Specifying the target Mart library and path as part of the prompt
Automatically save models to the specified Mart location without leaving the AI Copilot Conversation pane
Save models to the Mart root node when no library path is provided
Receive confirmation of the save operation directly in the AI Copilot Conversation pane

First Release

End-to-End Data Modeling

Support for conceptual, logical, and physical models within a single workflow
Traceability from business definitions through to implementation schemas
Forward and reverse engineering for leading data platforms (for example, Snowflake, Databricks, Microsoft Fabric Warehouse and Lakehouse)

AI-Assisted Modeling

Natural-language model generation from prompts
AI-assisted documentation and refactoring
Suggestions for relationships and joins

These capabilities help accelerate early-stage modeling while maintaining governance visibility within the Mart-backed environment.

Real-Time Collaboration

Multi-user, real-time model editing in a shared environment
Role-based access control and SSO/SAML support
Inline comments and collaboration on model elements

Versioning and Change Management

Centralized access through Quest erwin Mart Portal repository including model version control with check-in / check-out
Model comparison and merge capabilities
History tracking to support auditability and controlled evolution of models

Hybrid Coexistence with Quest erwin Data Modeler

Support for environments where Quest erwin Data Modeler and QDM are used together
Shared governance and migration path using the Mart repository
Enables gradual adoption without requiring immediate migration

QTDMP Platform Integration

Integration with the Quest Trusted Data Management Platform (QTDMP)
Connects modeling with governance, catalog, data quality, and data product workflows

Integration with Modern Data Stack

Integration with Git, dbt, and REST APIs
Designed to align with existing engineering and analytics workflows
Support for cloud-first development practices alongside centralized model management in Quest erwin Mart Portal

Quest Data Modeler Release Notes

Version 1.5

Version 1.4

Version 1.3

Version 1.0