Data Importers

2 posts · Nov 2019 – Oct 2025

What this trail covers

GitLab is continuously developing and improving its data importers to ensure a seamless and reliable experience for migrating data into GitLab or between GitLab instances. The team is focused on expanding the types of objects that can be migrated and enhancing the automation of the process. Group migration is the preferred method for its efficiency and ability to import multiple groups at once, while file-based import/export remains available for air-gapped or low-connectivity environments. Jira2Lab specifically addresses large-scale Jira migrations, handling complex custom fields, workflows, and integrating CI/CD pipelines.

All GitLab trails

Oct 2025How we built a structured Streamlit Framework in SnowflakeThis post details the creation of a structured Streamlit Application Framework within Snowflake, leveraging GitLab CI/CD for automated testing, code review, and deployment. The framework enforces security and compliance by design, with defined roles for maintainers, creators, and viewers, and automates environment preparation, dependency management, and application creation through templates and CI/CD pipelines. It introduces a YAML-based rule system for code compliance, including class, function, import, file, SQL, and share rules, and demonstrates automated environment setup and new page generation using make commands.
Jul 2025Migrating by direct transfer is generally availableThis post announces the general availability of direct transfer migration for GitLab groups and projects, a significant enhancement to the data import capabilities. It details the improvements over file exports, including automated migration of top-level groups with sub-groups and projects, post-import user contribution mapping, improved reliability for large projects through resource batching and concurrent execution, and better migration progress insights. It also provides examples of successful large-scale migrations and outlines best practices for preparation and execution, including migrating between recent versions and reviewing options to reduce migration duration. The post also mentions future work on supporting offline instances.
Nov 2024Streamline migrations with user contribution and membership mappingIntroduced post-import user contribution and membership mapping for group and project imports. This allows for reassignment of imported contributions to existing users after import, independent of email addresses, and requires user acceptance of the reassignment. This feature is available for direct transfer, GitHub, Bitbucket Server, and Gitea importers. Future improvements include CSV-based reassignment and enhanced UI visibility of placeholder limits.
Oct 2024Seamlessly migrate from Jira to GitLab with Jira2Lab at scaleThis post introduces Jira2Lab, a tool designed for large-scale migrations from Jira to GitLab. It addresses challenges such as massive data migration, custom field and workflow mapping, and the integration of CI/CD pipelines. Jira2Lab follows a five-phase methodology: discovery and planning, setup, pilot migrations, scaled migrations, and post-migration support. A case study highlights a successful migration of over 20,000 issues across 50 projects, demonstrating data integrity, CI/CD integration, and the effectiveness of pilot migrations.
Feb 2023How to migrate data to GitLab using main importersThis post details the functionality and usage of GitLab's data importers, focusing on group migration (direct transfer) and file-based import/export. It outlines the types of items imported for group migration (groups, subgroups, projects with feature flag enabled) and lists the specific components that can be imported from GitHub (repository description, Git data, branch protection rules, issues, pull requests, wiki pages, milestones, labels, comments, attachments, LFS objects, reviews, etc.). It also mentions the 'Repository by URL' import method. The post highlights the ongoing development by the group:import team to improve these importers.
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Jan 2023Moving projects easily: GitLab migration automation benefitsIntroduced a beta feature for direct project transfer migration, allowing automated migration of projects and their associated data between GitLab instances without manual file exports. This improves efficiency and correctly maps user associations, addressing a key limitation of the previous file export method. The feature is enabled by default on GitLab.com and configurable on self-managed instances. Future work includes improving efficiency and reliability for large projects, enhancing feedback during migration, enabling granular imports (subgroups, project selection), importing project relations, and automatically migrating users.
Apr 2020Why AppsFlyer moved from Bitbucket to GitLabThis post details AppsFlyer's migration from Bitbucket to GitLab. Key technical contributions include: development of a self-service Fast-Export wrapper for large-scale Mercurial to Git conversion, creation of a one-liner tool (BB2GL) connecting Slack for data migration status, implementation of scheduled tasks to track migration progress, development of an in-house API wrapper for GitLab metadata, and utilization of GitLab System Hooks for updating the API wrapper. The post also highlights the use of GitLab's data import tools for the final migration.
Nov 2019A framework for sssessing data organization maturityThis post introduces a framework for assessing data organization maturity, categorizing it into three tiers: reporting, insights, and predictions. It emphasizes that a mature data organization starts with a mature analytics organization, detailing the characteristics and value of each tier. The post also provides guidance on how to mature data teams through investment in team skills and tools (like DataOps and modern technologies), empowering the organization with data self-service, and leveraging open-source analytics and best practices for speed to value.