Jira to Snowflake

This page provides you with instructions on how to extract data from Jira and load it into Snowflake. (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?

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 Snowflake?

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

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.

Preparing data for Snowflake

Depending on your data structures, you may need to prepare your data before loading. Check the supported data types for Snowflake and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Loading data into Snowflake

Snowflake's documentation includes a Data Loading Overview that guides you through the task of loading your data. A data loading wizard in the Snowflake web UI may be useful if you're not loading a lot of data, but for many organizations, the limitations on that tool will make it unsuitable. You can load your data with two manual steps:

  • Use the PUT command to stage files.
  • Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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.

Other data warehouse options

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Panoply.

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 Snowflake data warehouse.