erwin was built for a different era of data

Modern data teams need more than static models.
SqlDBM gives you a shared, cloud-native foundation that teams can design, align, and scale data across Snowflake, Databricks, and dbt.

Trusted by data teams globally

400,000+

users globally

erwin wasn’t built for how data works today

erwin helped define an earlier era of data modeling built for centralized teams, controlled environments, and slower change. Today, data is distributed across cloud platforms like Snowflake and Databricks, transformation logic lives in tools like dbt, and teams span engineering, analytics, and business—making legacy modeling workflows harder to sustain.

Desktop tools slow down modern teams

Desktop-based tools limit access and create bottlenecks around licensed users and model ownership.

Managing environments shouldn’t break your models

Keeping dev, test, and production aligned in erwin is manual and error-prone. Changes overwrite each other and context gets lost.

Cloud workflows don’t map cleanly

Legacy modeling approaches weren’t designed for Snowflake, Databricks, or dbt-driven development.

Modeling shouldn’t depend on one person

In erwin, changes often flow through licensed model owners creating bottlenecks where teams wait on one person to make or approve changes.

Your data model shouldn’t be locked to specialists

In erwin, visibility often requires additional tools or manual exports, limiting access to a small group.

It’s hard to trust what changed and why

It’s hard to trust what changed and what’s in production. Changes overwrite each other, and teams lose confidence fast.

Move from erwin without starting over

SqlDBM gives you a shared, cloud-native architecture layer to define, align, and manage data across systems—connecting design to dbt, Snowflake, and Databricks so analytics and AI run on consistent definitions.

Import your erwin XML and bring your models with you: structure, relationships, and key metadata.

SqlDBM vs erwin: A Strategic Comparison

See how SqlDBM’s modern, cloud-native approach outperforms legacy desktop solutions

erwin Data Modeler

Deployment

Fully cloud-native, runs in the browser

erwin Data Modeler

Desktop-based, requires installation and setup

Collaboration

Real-time collaboration across teams with shared access

erwin Data Modeler

Limited to licensed users, workflows bottleneck around model owners

Versioning & Environments

Built-in version control with support for parallel workflows

erwin Data Modeler

Complex to manage changes across dev/test/prod

Cloud Data Platforms

Built for Snowflake, Databricks and BigQuery from the ground up

erwin Data Modeler

Not designed for modern cloud environments

dbt Alignment

Align models directly with dbt logic and structure

erwin Data Modeler

No native connection to transformation workflows

Visibility Across Teams

Central, accessible view for engineering, analytics, and business

erwin Data Modeler

Sharing often requires exports or additional tools

Handling Change

Clear visibility into changes and how systems stay aligned.

erwin Data Modeler

Changes can overwrite each other, hard to track impact

Scalability

Designed to scale with enterprise data environments

erwin Data Modeler

Performance and usability degrade with large models

Release Cadence

Monthly product updates

erwin Data Modeler

Limited

Migration Path

Import erwin XML and continue without rebuilding

erwin Data Modeler

Requires maintaining legacy workflows

Metadata Catalog Integration

Compatible with Atlan, Collibra, Confluence, Informatica

erwin Data Modeler

Collibra and Informatica Only

Trusted Cloud Platform Partners

Snowflake

Premier Partner

Databricks

Data Partner

Google Cloud

Partner

SqlDBM has become the backbone of our enterprise data warehouse by giving us a governed, documented design across Snowflake and surrounding databases. We now have a data model that we can trust and that scales with our business. When we launched a new business line, we extended our core model instead of starting from scratch, saving significant time and effort.

— Head of Data Engineering, Insurance

Bring your erwin models into SqlDBM

Import your XML, keep your structure, and skip the rebuild.

Platform

Data Modeling

SqlDBM AI Copilot

Model Governance

Snowflake Schema Monitoring

Integrations

See all

Why SqlDBM?

Have questions?

Resources

Strategic advisors

Kent Graziano

Gordon Wong

For cloud data platforms

Try modeling now

Data Modeling

SqlDBM AI Copilot

Model Governance

Snowflake Schema Monitoring

Integrations

See all

Why SqlDBM?

Have questions?

Try modeling now

Pricing

Security

erwin was built for a different era of data

Trusted by data teams globally

erwin wasn’t built for how data works today

Desktop tools slow down modern teams

Managing environments shouldn’t break your models

Cloud workflows don’t map cleanly

Modeling shouldn’t depend on one person

Your data model shouldn’t be locked to specialists

It’s hard to trust what changed and why

Move from erwin without starting over

SqlDBM vs erwin: A Strategic Comparison

erwin Data Modeler

Deployment

erwin Data Modeler

Collaboration

erwin Data Modeler

Versioning & Environments

erwin Data Modeler

Cloud Data Platforms

erwin Data Modeler

dbt Alignment

erwin Data Modeler

Visibility Across Teams

erwin Data Modeler

Handling Change

erwin Data Modeler

Scalability

erwin Data Modeler

Release Cadence

erwin Data Modeler

Migration Path

erwin Data Modeler

Metadata Catalog Integration

erwin Data Modeler

Trusted Cloud Platform Partners

Snowflake

Premier Partner

Databricks

Data Partner

Google Cloud

Partner

Bring your erwin models into SqlDBM

Cookies policy

Resources

Strategic advisors

Kent Graziano

Gordon Wong

For cloud data platforms

Try modeling now

Platform

Data Modeling

Model Governance

Snowflake Schema Monitoring

Integration