Jira to BigQuery

This page provides you with instructions on how to extract data from Jira and load it into Google BigQuery. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Jira?

Jira is an issue tracking tool with elements of agile project management woven into it. You can track progress, assign tasks, and introduce the results all from within the product. In short, Jira helps teams collaborate to get work done quickly.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With all of that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of Jira

For starters, you need to get your data out of Jira. That can be done by making calls to Jira’s REST API. The full documentation for the API can be found here.

To use the Jira REST API, your script needs to make HTTP requests, and parse the response. The Jira REST API uses JSON as its communication format. The standard HTTP methods like GET, PUT, POST and DELETE are going to be your major tools here.

Jira’s API offers access to data endpoints like issues, comments, and numerous other endpoints. Using methods outlined in the API documentation, you can retrieve the data you’d like to move to your destination database.

Sample Jira data

When you query the Jira API, it will return JSON formatted data. Below is 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

With the JSON in hand, you now need to map all those 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 data type (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Check out the Stitch Jira Documentation to get a good sense of what fields and data types will be provided by each endpoint. Once you have identified all of the columns you will want to insert, go ahead and create a destination table in your database where this data can be loaded.

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Keeping Jira data up to date

So what’s next? You've got a script that collects data from Jira and puts it where you want. This is where a lot of Jira ETL projects can fall apart. You worked hard on this script, and it only pays off if you can use it down the road.

First, you need to account for new data being generated in Jira. Look through the data you are getting from Jira and find a field that is automatically incremented such as updated_at or created_at. Build your script to use these fields as a bookmark for finding new or updated data. Second, you need to get your script running continuously. Some folks use a loop or a cron job.

Other data warehouse options

BigQuery is really great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Postgres or Redshift, which are two RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading this data into Postgres or Redshift, check out To Redshift and To Postgres.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Jira data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.