GitLab to Power BI

This page provides you with instructions on how to extract data from GitLab and analyze it in Power BI. (If the mechanics of extracting data from GitLab seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is GitLab?

GitLab offers a web-based Git repository manager with version control and issue tracking features.

What is Power BI?

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

Getting data out of GitLab

GitLab provides a REST API, but it says, "Going forward, we will start on moving to GraphQL and deprecate the use of controller-specific endpoints."

Most of the items stored in GitLab are accessible through the API. Dozens of items are on the list, including merge requests, project milestones, and todos. As an example, to get a list of repository branches for a particular project, you could call GET /projects/[id]/repository/branches.

Sample GitLab data

GitLab returns information in JSON format. Each JSON object may contain more than a dozen attributes, which you have to parse before loading the data into your data warehouse. Stitch provides documentation on some of the GitLab table schemas. Here's an example of what some of the data for that call to return all tickets might look like:

[
  {
    "name": "master",
    "merged": false,
    "protected": true,
    "developers_can_push": false,
    "developers_can_merge": false,
    "commit": {
      "author_email": "john@example.com",
      "author_name": "John Smith",
      "authored_date": "2012-06-27T05:51:39-07:00",
      "committed_date": "2012-06-28T03:44:20-07:00",
      "committer_email": "john@example.com",
      "committer_name": "John Smith",
      "id": "7b5c3cc8be40ee161ae89a06bba6229da1032a0c",
      "short_id": "7b5c3cc",
      "title": "add projects API",
      "message": "add projects API",
      "parent_ids": [
        "4ad91d3c1144c406e50c7b33bae684bd6837faf8"
      ]
    }
  },
  ...
]

Preparing GitLab data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. GitLab's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Power BI

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

Analyzing data in Power BI

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

Keeping GitLab data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in GitLab.

And remember, as with any code, once you write it, you have to maintain it. If GitLab modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to. If GitLab makes the REST API obsolete and moves ahead solely with GraphQL, you may have to start from scratch.

From GitLab to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing GitLab data in Power BI is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites GitLab to Redshift, GitLab to BigQuery, and GitLab to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your GitLab data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Power BI.