Jira to Metabase

This page provides you with instructions on how to extract data from Jira and analyze it in Metabase. (If the mechanics of extracting data from Jira 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 Jira?

Atlassian's Jira is an issue-tracking tool with collaboration and elements of agile project management woven into it. You can track progress, assign tasks, and introduce results all from within the product.

What is Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

Getting data out of Jira

You can get your data out of Jira by using Jira's REST API, which offers access to issues, comments, and numerous other endpoints. For example, to get data about an issue, you could call GET /rest/api/2/issue/[issueIdOrKey].

Sample Jira data

The Jira API returns JSON-format data. Here's an example response from the issues endpoint.

{
    "expand": "schema,names",
    "startAt": 0,
    "maxResults": 50,
    "total": 6,
    "issues": [
        {
            "expand": "html",
            "id": "10230",
            "self": "http://kelpie9:8081/rest/api/2/issue/BULK-62",
            "key": "BULK-62",
            "fields": {
                "summary": "testing",
                "timetracking": null,
                "issuetype": {
                    "self": "http://kelpie9:8081/rest/api/2/issuetype/5",
                    "id": "5",
                    "description": "The sub-task of the issue",
                    "iconUrl": "http://kelpie9:8081/images/icons/issue_subtask.gif",
                    "name": "Sub-task",
                    "subtask": true
                },
.
.
.
                },
                "customfield_10071": null
            },
            "transitions": "http://kelpie9:8081/rest/api/2/issue/BULK-62/transitions",
        },
        {
            "expand": "html",
            "id": "10004",
            "self": "http://kelpie9:8081/rest/api/2/issue/BULK-47",
            "key": "BULK-47",
            "fields": {
                "summary": "Cheese v1 2.0 issue",
                "timetracking": null,
                "issuetype": {
                    "self": "http://kelpie9:8081/rest/api/2/issuetype/3",
                    "id": "3",
                    "description": "A task that needs to be done.",
                    "iconUrl": "http://kelpie9:8081/images/icons/task.gif",
                    "name": "Task",
                    "subtask": false
                },
.
.
.
                  "transitions": "http://kelpie9:8081/rest/api/2/issue/BULK-47/transitions",
        }
    ]
}

Preparing Jira data

Once you have the JSON in hand, you need to map the data fields into a schema that can be inserted into your database. This means that, for each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Check out the Stitch Jira Documentation to get a sense of what fields and datatypes are provided by each endpoint. Once you've identified all of the columns you want to insert, you can create a destination table in your database into which to load the data.

Loading data into Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

Keeping Jira 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 Jira.

And remember, as with any code, once you write it, you have to maintain it. If Atlassian modifies Jira's 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.

From Jira to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Jira data in Metabase 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 Jira to Redshift, Jira to BigQuery, Jira to Azure Synapse Analytics, Jira to PostgreSQL, Jira to Panoply, and Jira to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Jira with Metabase. With just a few clicks, Stitch starts extracting your Jira data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.