Changelog

Model, reverse engineer, and forward engineer Teradata in SqlDBM, and bring it into the context layer powering your AI initiatives.

Main is no longer the only option. SqlDBM now lets you merge into any branch you’re a member of! You can also create a new branch from any change branch, so you can build on a teammate’s in-progress work without waiting for it to reach Main.

Reverse engineering is now supported for named semantic models and SqlDBM semantic defaults.

Seamlessly manage virtual relationships directly from your spreadsheets by exporting them alongside physical constraints.

The iframe integration now supports filtering by revision flags, allowing you to display only flagged or approved revisions to your audience.

Branch merging is no longer an all-or-nothing operation at the table level. With our new merge dialog, you can now selectively include or exclude individual column-level changes.

The Get Revision List API endpoint now includes the recently-released Approval and comment fields in addition to all other revision flags.

Revisions now have more metadata fields to record approval status and comments so your team (and your AI agents) have more context on why a change was made and who reviewed it.

Liquibase forward engineering lets teams generate version-controlled changelogs directly from their SqlDBM models, closing the gap between design and automated deployment.

We’ve introduced a new “Whole word” toggle for glossary mapping rules to give you greater precision when enforcing naming standards across your projects.

Column and table templates, Excel export, subtype/supertype hierarchies in logical models, and metadata exploration (what, when, who changed).

Editable view columns, views in database documentation, and more!

All databases with direct connect now supported in User Connections

Allocate AI credits across users from a shared organizational pool, with visibility into assigned, consumed, and remaining balances.

Route account-level notifications to Slack for team visibility while maintaining security alerts via email.

Design a governed, AI-ready semantic layer directly alongside their physical model and forward-engineer it into Snowflake Semantic YAML

The SqlDBM Copilot Context Folder processes uploaded files (Excel, CSV, SQL, Images, PDF) to extract and analyze data models, DDL, documentation, and diagrams.

The SqlDBM Copilot now adds AI-generated summaries to the Compare Revisions and Forward Engineering screens.

Your data model, now readable by AI. The model your team built in SqlDBM becomes the source of truth your AI reads its context from.

Auto-generate Snowflake views and tables from YAML templates, reducing SQL effort from hours to seconds.

Full support for ARRAY and RANGE types (including arrays of structs), property-level Compare that goes beyond plain-text diffing, and editable columns and descriptions on views.

Views now support Virtual Primary Keys and Virtual Business Keys

Forward Engineering now adds quick-filters to instantly isolate dbt sources and models

Snowflake Excel upload support for Dynamic, Hybrid and Iceberg tables

Views and Dynamic Tables now support visible, editable data types as metadata

For automated, system-driven project changes

Redshift news and native syntax support

SqlDBM now supports database.schema.object naming for greater modeling precision.

You can now group projects into dashboard folders with unlimited nesting.

Uploading a dbt manifest now creates virtual dependency relationships from dbt refs.

Connect physical objects back to logical models.

Embed documentation anywhere.

Bridge the gap between your data models and dbt projects with dbt Manifest Import

Model logical joins and data lineage with new virtual relationships

The FE screen is now more streamlined and intuitive

Improved auto-layout, snap-to-grid, alignment guides, and full line reshaping!

Fine-tune word boundaries for control over underscores, numbers, and case changes

FK linking respects column position, and Explorer menus allow Shift+Click multi-select

An AI data modeling assistant that supercharges your productivity

Migrate objects, diagrams, and subject areas from ERwin to SqlDBM

Support for select-all (Ctrl+A) and multiselect (Shift+click) on columns

Flag objects to ignore naming, case, or glossary rules

Iceberg, Dynamic, and Hybrid tables supported in Database Documentation and compare

Fields and Pages editing and merging in Concurrent Working branches

Full native support for all catalog types and properties

Full native support for all properties and indexes

Full native support for Dynamic tables, their logic, and properties

Exclude items from Global Reference at object or schema level

Drag-and-drop functionality for objects in Diagram Explorer

Cancel/reselect revisions mid-compare and schema filtering on Database Documentation

Map predefined standards to your existing projects

Navigable links can now be included alongside your objects

Model Governance fields are now easily accessible directly from the diagram

View existing relationships among global objects without needing to navigate away from the local project

Performance and visual improvements using properties instead of text

Apply rules to physical and/or logical names or set inactive

Case standards for logical names

iFrame embeds for projects in third-party systems

Snowflake views on the diagram with editable columns and parameters

Enhanced foreign key linking experience

Snowflake revamped object parser offers faster performance

Push dbt source & model YAML to git in Forward Engineering

Added support for multiple repos in push-to-git

Simplified UI for Foreign Keys and Relationships