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Start by exporting the data from GitLab. Depending on whether you need repository data or other project-related information, navigate to the GitLab UI. For repositories, clone the repository locally using the `git clone` command. If you need additional project data, use GitLab's export feature found in the project settings to download the necessary data as a `.tar.gz` file.
Once your data is exported, prepare it for transfer. If you cloned a repository, ensure all relevant files are included. For project exports, extract the `.tar.gz` file to access the data files. Organize the data in a structured format that's compatible with Starburst Galaxy, such as CSV or JSON.
Log into your Starburst Galaxy account and set up the necessary environment for data ingestion. This involves creating a catalog and schema that will house your data. For example, create a new schema within the default catalog or an existing catalog where your data will reside.
Since Starburst Galaxy operates on cloud storage, upload your prepared data to a compatible cloud storage service like AWS S3, Google Cloud Storage, or Azure Blob Storage. Use the cloud provider�s web interface or command-line tools to upload the files. Ensure the data files are accessible by making them public or setting appropriate permissions.
In Starburst Galaxy, configure access to the cloud storage where your data is located. This involves setting up a connection from Starburst Galaxy to your cloud storage. You�ll need to provide access credentials and define the external location in your Starburst Galaxy setup.
With access configured, create external tables in Starburst Galaxy that point to your data in the cloud storage. Use the Starburst Galaxy interface or SQL commands to define tables with the correct schema, pointing to the cloud storage file paths. Ensure data types in the table definitions match the formats in your data files.
Finally, verify that the data has been correctly set up by running sample queries in Starburst Galaxy. Check the integrity and completeness of the data by comparing it to the original data from GitLab. Use SQL queries to perform data validation and ensure that the data is accurately reflected in your Starburst Galaxy environment.
By following these steps, you can manually move data from GitLab to Starburst Galaxy without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
GitLab is web-based Git repository manager. Whereas GitHub emphasizes infrastructure performance, GitLab’s focus is a features-oriented system. As an open-source collaborative platform, it enables developers to create code, review work, and deploy codebases collaboratively. It offers wiki, code reviews, built-in CI/CD, issue-tracking features, and much more.
GitLab's API provides access to a wide range of data related to a user's GitLab account and projects. The following are the categories of data that can be accessed through GitLab's API:
1. User data: This includes information about the user's profile, such as name, email, and avatar.
2. Project data: This includes information about the user's projects, such as project name, description, and visibility.
3. Repository data: This includes information about the user's repositories, such as repository name, description, and access level.
4. Issue data: This includes information about the user's issues, such as issue title, description, and status.
5. Merge request data: This includes information about the user's merge requests, such as merge request title, description, and status.
6. Pipeline data: This includes information about the user's pipelines, such as pipeline status, duration, and job details.
7. Job data: This includes information about the user's jobs, such as job status, duration, and artifacts.
8. Group data: This includes information about the user's groups, such as group name, description, and visibility.
Overall, GitLab's API provides access to a comprehensive set of data that can be used to automate and streamline various aspects of a user's GitLab workflow.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
