Changelog

Push model metadata from SqlDBM straight into Collibra as governed assets, so definitions live in one place instead of being maintained twice. Map SqlDBM fields to attributes that already exist in your Collibra instance, pick the target community and domain, and choose exactly which objects are included. Pushes run as an asynchronous import job you can monitor from the toolbar, and each project can keep several mappings to send different scopes to different domains.

Create reusable AI workflows that capture your organization’s standards, governance rules, and review processes. From documentation and SQL generation to PII detection and model validation, Copilot helps every team follow the same trusted process at scale.

Run all SqlDBM AI through your own model provider and API keys, with support for Anthropic, OpenAI, and Google Gemini. Configure it once in AI Settings by choosing a provider, entering your key, and selecting a model, and Copilot switches over within seconds. Configuration is applied per company and consumes no SqlDBM AI credits, so usage and spend stay under your control. BYOM is enabled for your company by your Customer Success Manager before it can be configured.

Copilot now runs directly on the Semantic Model page, so it always works against the model you have open instead of the project default. Create and switch between semantic models, add tables and views, set business names and descriptions, and configure semantic properties including semantic type, synonyms, sample values, and column exposure. Edits inside a named model override inherited defaults without changing the underlying physical objects, and Copilot reports when an action cannot be completed rather than claiming success.

AI tools connected to SqlDBM can now contribute changes to governed models: create a pending revision, fork a branch, or create a new project from a DDL script. Write access is always explicit — the scope must be approved when you connect, and a read-only token cannot change anything. Governance still applies: agents cannot merge, approve, or delete revisions, and every write uses the caller’s own permissions and lands in the revisions list with their name and timestamp.

Approval Process for concurrent working is here, giving teams a structured, role-based review step before any branch can be merged to Main. This ensures that data model changes are validated by the right people before they reach production. Project owners can enable approvals on any concurrent working project and designate Approvers who review, adjust, and either approve or reject pending merge requests, with automatic notifications keeping all branch members informed at every stage.

Data teams can now programmatically govern their semantic layers using SqlDBM’s public API with GET and POST methods, facilitating effortless integration into CI/CD pipelines and Git-based version control. You can export semantic YAML directly from any project revision to commit it to source control, and then automate updates back to SqlDBM to maintain synchronization. This functionality removes the manual overhead of UI-based management, providing a robust, repeatable workflow for maintaining verified, high-quality definitions at scale.

You can now create a new SqlDBM project directly from an ERwin export, making it easier to bring existing data models into your workflow. This streamlines migration, reduces manual rework, and helps teams start delivering value faster. Simply import your ERwin XML file and continue working in SqlDBM with the target database platform detected automatically.

SqlDBM now generates dbt YAML in Fusion format by default, helping teams adopt the latest dbt workflows with output that is ready to use and free from legacy-format errors and warnings. Teams can continue using the Legacy option for older dbt environments, ensuring existing pipelines remain supported during the transition. The new format is also available in the API as a parameter, with “legacy” set as the default to ensure no disruptions to existing workflows.

You can now organize and categorize your BigQuery resources directly within SqlDBM using native Labels on tables, views, materialized views, and datasets. Lables make it easy to filter, search, and manage large projects by environment, team, domain, or any convention your organization uses. Labels are fully reusable across objects so teams can enforce consistent classification at scale without repetitive manual work.

SqlDBM now supports Databricks tags, letting teams organize, classify, and search their Unity Catalog schemas, tables, views, and columns directly within their data models. You can create reusable tags, assign them in bulk, filter diagrams, and keep everything in sync through forward and reverse engineering. This brings the same consistent tagging experience already available for Snowflake and BigQuery to Databricks users, making it easier to enforce governance standards and find what matters across large catalogs.

The new Global Reference Summary gives you a clear view of which projects are referencing your Global Objects. Available on all Global-enabled projects, the summary table provides sortable details and summary counts make it easier to identify dependencies, coordinate with stakeholders, and avoid unexpected disruptions.

Users now have full control over how primary key columns are positioned within their tables instead of PK columns automatically jumping to the top. Managing and creating primary keys is also more intuitive, with a streamlined constraint menu that lets you add a PK directly in one step, just like any other constraint type. These improvements give you a cleaner, more predictable modeling experience where your table structure stays true to your design intent, both in the diagram and in the generated DDL.

Semantic modeling is now available in Logical projects, helping teams add business meaning to their data earlier in the design process. Users can define semantic defaults once and carry them through forward engineering and Logical-to-Physical import/export workflows, reducing duplicate work and preserving consistency. This continuity makes models easier to understand, maintain, and use for analytics across the development lifecycle.

Semantic Models are now fully supported in Concurrent Working: every team member can create and edit semantic models in their own branch, then review changes in a dedicated Semantic Models tab of the merge screen with a side-by-side, line-highlighted YAML comparison. Your semantic layer gets the same review-and-select merge discipline as SQL objects and diagrams, so multiple modelers can finally work on it in parallel without overwriting each other. Note that a semantic model must be merged together with the objects it references: if its related objects are excluded, the semantic model won’t be merged.

Global References now extend to Logical projects: reference objects across Logical projects or bring logical objects into physical projects or vice versa. Model a shared entity once and reference it everywhere and carry the source project’s icon so your architecture stays readable. Connect your business-level model directly to its physical implementations and give your whole organization one connected picture.

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