With Portable, integrate Jira data with your Redshift warehouse in minutes. Access your issue-tracking product data from Redshift without having to manage cumbersome ETL scripts.
The Two Paths to Connect Jira to Amazon Redshift
There are two ways to sync data from Jira into your data warehouse for analytics.
Method 1: Manually Developing a Custom Data Pipeline Yourself
Write code from scratch or use an open-source framework to build an integration between Jira and Redshift.
Method 2: Automating the ETL Process with a No-Code Solution
Leverage a pre-built connector from a cloud-hosted solution like Portable.
How to Create Value with Jira Data
Teams connect Jira to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities Jira exposes via their API, outline insights you can build with the data, and summarize the most common analytics environments that teams are using to process their Jira data.
Extract: What Data Can You Extract from the Jira API?
Jira is a issue-tracking product used for planning and managing cross-functional projects.
To help clients power downstream analytics, Jira offers an application programming interface (API) for clients to extract data on business entities. Here are a few example entities you can extract from the API:
You can visit the Jira API Documentation to explore the entire catalog of available API resources and the complete schema definition for each. As an example, here are some of the details for the projects endpoint in the Jira API documentation.
As you think about the data you will need for analytics, don’t forget that Portable offers no-code integrations to other similar applications like Monday.com, Trello, and Shortcut that can be useful for comparison purposes.
Regardless of the SaaS solution you use, it’s important to find a issue-tracking product with robust data available for analytics.
Load: Which Destinations Are Best for Your Jira ETL Pipeline?
To turn raw data from Jira into dashboards, most companies centralize information into a data warehouse or data lake. For Portable clients, the most common ETL pipelines are:
- Jira to Snowflake Integration
- Jira to Google BigQuery Integration
- Jira to Amazon Redshift Integration
- Jira to PostgreSQL Integration
Once you have a destination to load the data, it’s common to combine Jira data with information from other enterprise applications like Mailchimp, HubSpot, Zendesk, and Klaviyo.
From there, you can build cross-functional dashboards in a visualization tool like Power BI, Tableau, Looker, or Retool.
Develop: Which Dashboards Should You Build with Jira Data?
Now that you have identified the data you want to extract, the next step is to plan out the dashboards you can build with the data.
As a process, you want to consume raw data, overlay SQL logic, and build a dashboard to either 1) increase revenue or 2) decrease costs.
Replicating Jira data into your cloud data warehouse can unlock a wide array of opportunities to power analytics, automate workflows, and develop products. The use cases are endless.
Now that we have a clear sense of the insights we can create, let’s compare the process of developing a custom Jira integration with the benefits of using a no-code ETL solution like Portable.
Method 1: Building a Custom Jira ETL Pipeline
To build your own Jira integration, there are three steps:
- Navigate the Jira API documentation
- Make your first API request
- Turn an API request into a complete data pipeline
Let’s walk through the process in more detail.
How to Interpret Jira’s API Documentation
When reading API documentation, there are a handful of key concepts to consider.
There are many common authentication mechanisms. OAuth 2.0 (Auth Code and Client Credentials), API Keys, JWT Tokens, Personal Access Tokens, Basic Authentication, etc. For Jira, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.
Jira offers a few different ways of authenticating depending on the type of application you are building. If you are just getting started, you can use basic authentication; however, they also offer authentication mechanisms tailored to Forge Apps and Connect Apps.
It’s important to identify the Jira API endpoints you want to use for analytics. Most APIs offer a combination of GET, POST, PUT, and DELETE request methods; however, for analytics, GET requests are typically the most useful. At times, POST requests can be used to extract data as well.
For Jira, the projects endpoint is a great place to get started.
For each API endpoint you would like to use for analytics, you need to understand the method (GET, POST, PUT, or DELETE) and the URL, but there are other considerations to take into account as well. You should look out for pagination mechanics, query parameters, and parameters that are added to the request path.
Some API endpoints offer pagination. When you are paginating through responses, the API response will include metadata on pagination such as the startAt parameter (where you are starting the request), the maxResults parameter (how many results to return), and the isLast parameter (whether or not you are on the last page).
Some API endpoints require unique identifiers from a previous API response to be included in the URL path.
For instance, to get all users for a group, you need a group ID that is returned from another endpoint.
How Do You Call the Jira API? (Tutorial)
- Follow the instructions above to read the Jira API documentation
- Identify and collect your credentials for authentication
- Pick the API resource you want to pull data from
- Configure the necessary parameters, method, and URL to make your first request (e.g. with curl or Postman)
- Add your credentials and make your first API call . Here is an example request using curl (without real credentials):
curl --request GET \ --url 'https://your-domain.atlassian.net/rest/api/3/project/search' \ --user '[email protected]:<api_token>' \ --header 'Accept: application/json'
How Do You Maintain a Custom Jira to Redshift ETL Pipeline?
Making a call to the Jira API is just the beginning of maintaining a complete custom ETL pipeline.
Here is a getting-started guide to building a production-grade pipeline for Jira:
- For each API endpoint, define schemas (which fields exist and the type for each)
- Process the API response and parse the data (typically parsing JSON or XML)
- Handle and replicate nested objects and custom fields
- Identify which Jira fields are primary keys and which keys are required vs. optional
- Version control your changes in a git-based workflow (using GitHub, GitLab, etc.)
- Handle code dependencies in your toolchain and the upgrades that come with each
- Monitor the health of the upstream API, and —when things go wrong— troubleshoot via the status page, reach out to support, and open tickets
- Handle error codes (HTTP error codes like 400s, 500s, etc.)
- Manage and respect rate limits imposed by the server
We won’t go into detail on all of the items above, but rate limits are a great example of the complexity found in a production-grade data pipeline.
For rate limits, Jira recommends watching for 429 HTTP error codes and retry-after headers to inform how best to make requests.
If you don’t respect rate limits, and if you can’t handle server responses (like 429 errors with a Retry-After header), your pipeline can break, and analytics can become out-of-date.
What Are the Drawbacks of Building the Jira ETL Pipeline Yourself?
You can probably tell at this point that there is a lot of work that goes into building and maintaining an ETL pipeline from Jira to your data warehouse.
If you want less development work, faster insights, and no ongoing responsibilities, you should consider a cloud-hosted ETL solution.
Let’s walk through the setup process for a no-code ETL solution and its benefits.
Method 2: Using a No-Code Jira ETL Solution
No-code ETL solutions are simple. Vendors specialize in building and maintaining data pipelines on your behalf. Instead of starting from scratch for each integration. Companies like Portable create connector templates that can be leveraged by hundreds or thousands of clients.
Step-By-Step Tutorial for Configuring Your Jira ETL Pipeline
Off-the-shelf ETL tools offer a no-code setup process. Here are the instructions to connect Jira to your cloud data warehouse with Portable.
- Create an account (no credit card required)
- Add a source —search for and select Jira
- Authenticate with Jira using the instructions in the Portable console
- Select Redshift and authenticate
- Set up a flow connecting Jira to your analytics environment
- Run your flow to replicate data from Jira to your warehouse
- Use the dropdown to set your data flow to run on a cadence
What Are the Benefits of Using Portable for Jira ETL?
Start moving Jira data in minutes. Save yourself the headaches of reading API documentation, writing code, and worrying about maintenance. Leave the hassle to us.
Easy to Understand Pricing
With predictable, fixed-cost pricing per data flow, you know exactly how much your Jira integration will cost every month.
Fast Development Speeds
Access lightning-fast connector development. Portable can build new integrations on-demand in hours or days.
APIs change. Schemas evolve. Jira will have maintenance issues and errors. With Portable, we will do everything in our power to make your life easier.
Unlimited Data Volumes
You can move as much data from Jira to Amazon Redshift as you want without worrying about usage credits or overages. Instead of analyzing your ETL costs, you should be analyzing your data.
Free to Get Started
Sign up and get started for free. You don’t need a credit card to manually trigger a data sync, so you can try all of our connectors before paying a dime.