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GIT Repository

A Git repository is a distributed version control system used to track and manage changes to files over time. Git was created in 2005 by Linus Torvalds, the creator of Linux, and remains an open-source project maintained by a global developer community. Originally designed to manage source code, Git has evolved into a widely used system for versioning all types of digital artifacts—including scripts, documentation, configuration files, and data modeling outputs.

Git enables teams to collaborate effectively by maintaining a complete, chronological history of all changes made to files. It supports branching and merging, allowing multiple users to work in parallel without overwriting each other’s contributions. Teams can revert to previous versions, compare differences between commits, and trace who made each change. Git repositories are commonly hosted on platforms such as GitHub, GitLab, Bitbucket, and Azure Repos, which provide interfaces and additional collaboration features on top of Git’s core functionality.

Within ER/Studio, a Git repository is used to manage and version-control exported modeling artifacts, such as Create DDL scripts and JSON files generated from physical data models. Starting in version 20.3 of the Enterprise Edition, ER/Studio introduced native Git integration that allows users to connect directly to a Git repository, generate model outputs, and commit those artifacts without leaving the modeling environment.

This integration enables modelers and data architects to:

  • Commit DDL and JSON exports directly from ER/Studio into Git repositories
  • Compare physical data models with previous Git versions to detect changes
  • Generate and commit ALTER scripts for accurate, auditable schema updates
  • Align data modeling activities with DevOps workflows, including CI/CD pipelines
  • Maintain an audit trail of changes to modeling artifacts for compliance and governance

By combining ER/Studio’s modeling capabilities with Git’s version control, teams gain greater visibility, traceability, and collaboration across the entire data architecture lifecycle. This integration supports enterprise-wide initiatives where data modeling, automation, and software development processes need to stay in sync.

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