Showing results for 
Search instead for 
Did you mean: 
Sign up Log in

Jira BigQuery Integration: How to Load Jira Data into BigQuery

Jira BigQuery Integration can help businesses speed up their data processing, quicker get valuable insights, make more informed decisions, and improve collaboration across teams. In this article, we will show how to enable the export of information from Jira and upload data to BigQuery.

Jira BigQuery Integration Advantages

What is BigQuery? It's a cloud-based data warehouse service provided by Google that allows you to store and analyze large amounts of data using SQL-like queries. You can upload data, create tables, and run queries using Google's web interface or APIs. It's like having your database server without the hassle of maintaining it yourself. 

Just imagine how you can leverage your business reporting, forecasting, and data visualization if you connect BigQuery to Jira. Here are some of the critical reasons why BigQuery integration is vital for Jira data:

Centralized Data Storage

By integrating Jira data into BigQuery, businesses can consolidate data from multiple sources into a centralized location. It can help ensure that all teams have access to the same data and work together more effectively, as well as reduce the risk of data silos.

Improved Data Analysis

BigQuery provides a SQL-like interface, making it easy for businesses to write and execute complex queries on Jira data. It allows users to create custom reports and dashboards that provide deeper insights into the Jira data they need to make informed decisions and improve project management, team productivity, and customer satisfaction.


BigQuery can handle large amounts of data, making it easier for businesses to analyze Jira data at scale. With BigQuery, companies can store and process terabytes of Jira data in seconds without harm to performance.

Real-time Data Processing

BigQuery is designed to provide fast query performance, enabling users to analyze Jira data and gain insights in real-time quickly. It can be beneficial for businesses that need to always have up-to-date data at hand and make timely decisions based on the latest project information.

Data Security

BigQuery provides robust industry-standard security features, including encryption and access controls, to help ensure that Jira data is stored securely. It can be significant for businesses that handle sensitive data.

Advanced-Data Visualization

You can use BigQuery's built-in visualization tools, such as charts, graphs, dynamic ranks, and maps, to create interactive dashboards and reports based on your Jira data. You can also integrate BigQuery with other Google Cloud services, such as Data Studio, or third-party solutions like Looker to create even more customized visualizations.

Cross-Functional Insights

Analyzing Jira data in combination with data from other sources in BigQuery can provide valuable insights into the impact of your projects on different areas of your business. For example, by combining Jira data with sales data from your CRM system, you can see how your project management approach impacts revenue and customer satisfaction. It will help your team to collaborate more effectively and make better strategic decisions.

Automated Data Integration

The data is loaded into BigQuery automatically, eliminating the need for manual data integration processes. It can help you save time and reduce the risk of errors. The BigQuery Connector for Jira also provides real-time updates, so you can access the latest Jira data in BigQuery as soon as it is available.

4 Methods for Loading Jira Data into BigQuery

When connecting Jira to BigQuery, multiple methods are available for loading Jira data into BigQuery. The methods mentioned below offer a range of choices and adaptability to merge the two platforms smoothly.

Custom API-based Integration

This method involves building a custom integration between Jira's API and BigQuery's API to extract Jira data and load it into BigQuery. It offers excellent flexibility and control over the data extraction and transformation process. Hence, it can be especially effective for complex use cases that require a high degree of customization. However, this method requires programming skills and can be time-consuming to set up and maintain.

Custom ETL (Extract, Transform, Load) pipeline

Loading Jira data into BigQuery through a custom ETL (Extract, Transform, Load) pipeline helps extract data from Jira, transform it into a format compatible with BigQuery, and load it into the platform. This method allows you to implement complex data integration scenarios, perform additional data transformations, and integrate data from multiple sources. However, it requires programming and ETL skills and can be time-consuming to set up and maintain.

Data Export and Import

It is also possible to export and import the data manually. Jira allows you to export data in CSV, XML, or JSON format, which can then be uploaded to BigQuery. You can use the Google Cloud Console, the BigQuery command-line tool, and BigQuery Data Transfer Service to create a new table and upload the exported data. This method is the most time-consuming and does not allow you to automate regular data updates.

Alpha Serve's BigQuery Connector for Jira

BigQuery Connector for Jira allows you to perform a Jira BigQuery integration quickly and easily. You can start loading data from Jira Software, Jira Service Management, Jira Work Management, and Leading Atlassian Marketplace Apps data into BigQuery after performing simple installation and set-up steps. The app automates the data flow and allows one to schedule the updates and see the changes in real time.

How to Load Jira Data to BigQuery with Alpha Serve's BigQuery Connector for Jira

Alpha Serve's BigQuery Connector for Jira offers several benefits for organizations looking to integrate Jira data into BigQuery. It automates the data integration process between Jira and BigQuery, eliminating the need for manual data exports and uploads. BigQuery Connector for Jira allows fetching Jira Core, Jira Work Management, Jira Software & Service Management fields, including Custom fields, History, and Agile, as well as calculated fields such as Time at Current Assignee, Time at Assignee, and Time in Status. 

The connector is designed to handle large volumes of data and can be scaled up or down as required. It supports data Export from Leading Marketplace Apps, such as Tempo Timesheets, Tempo Cost Tracker, Projectrak, Advanced Roadmaps, etc. It boasts advanced filtering options so that you may always narrow your data selection. It also supports real-time data syncing, meaning that data is updated in BigQuery near-real-time.

Step 1. Install BigQuery Connector for Jira

Please note that you need Jira Cloud Administrator rights to follow the instructions provided on this page. For more information, please read here

In Jira navigate to Jira Cloud Apps and select the Explore more apps section. Find BigQuery Connector for Jira Alpha Serve with the search field provided and hit it. Click Try it free to get a 30-day free trial license. Follow the instructions until the installation process is over.


When all is set, you will find BigQuery Connector for Jira by navigating Apps → BigQuery Connector for Jira


You can also install BigQuery Connector for Jira Cloud directly from Atlassian Marketplace. You will be able to choose the Cloud hosting option. If you aren't a Jira administrator, you can ask your Jira admin to Request install app.


Step 2. Configure BigQuery Connector for Jira

Before starting to work with the app, you should perform some configurations. For example, you can manage access to the app in the BigQuery Connector for the Jira Administration section. The screen allows you to overview granted permissions, add or remove users or groups who can work with the connector, or entirely prohibit using the app to all.


To manage the access, navigate to the Edit permissions window, and click the Select Groups field. Add as many available options as you need. You can select particular users in the Select Users field.


Don’t forget to Save the changes before leaving the page.

BigQuery Connector for Jira supports loading data to BigQuery from different add-ons available at the Atlassian marketplace, but you need to configure each separately. Instructions can be found here.

Step 3. Create a Service Account Key in BigQuery

To use a service account outside of Google Cloud, you need a service account key. To create one, you should Authorize in Google Cloud Platform and Create a project, confirm it, and enable API.


When done, go to the APIs & Services tab and choose Credentials. Click Create Credentials and choose Service account. Add details. Email addresses are generated automatically. Simply Copy it and continue the process.


Grant this service account access to the project, then set the permissions. When you finish, follow the Manage service account link, hit the Actions button, and select the Manage keys option.

Press the ADD KEY button and select Create new key. Select JSON key type and click Create. The created key will be downloaded automatically. 


Step 4: Create a Jira Data Source

You need a Jira API token to start working with BigQuery Connector for Jira. To create it follow these steps:

  1. Navigate to your Account settings.
  2. Find and click the "Create and manage API tokens" link in the Security tab.
  3. Click the "Create API token" button.
  4. Provide a name for your API token and click Create.
  5. Ensure you copy your newly generated API token, as it will not be visible again.
  6. Go to Apps > BigQuery Connector for Jira > Tokens > Jira API token and enter the copied token. Finally, click on "Validate & Save."

Then continue with data source creation by navigating to BigQuery Connector for Jira.


On the data source creation screen, you will be asked to fill in several sections. In the Title section, enter the names of the data source and dataset, service account key, description, set share settings, and select groups and users.


In the Filter Issues section, you can choose to export All the data or use the filters. 


Or choose Select by JQL to create a custom request and filter data. JQL provides flexible search capabilities for issues, projects, and more in Jira. Enter your JQL expression and click "Submit JQL" to apply it.


Also you can choose Basic to use standard field filters. Click the "Issue filter" button to set up a basic filter. Choose from the available options such as projects, issue types, statuses, created dates, and updated dates. Apply the filter by clicking "Apply."


Advanced filters for Jira Software fields are also available.

To narrow your data selection, you can use such tools as a search field, magnifier, tabs with Jira Fields, Issues, and Checkboxes. When ready, you can preview and Save the selection, or close it without applying changes.

Step 5: Load Jira Data to BigQuery

You must set up the service account key to proceed with data export. On the data source creation screen, hit the Export data button and upload the service account key .json file. Click Submit. A confirmation message will indicate that the service account key has been successfully uploaded.


Click on the "Export Data" button. The data export process will be completed once it reaches the "NOT EXPORTED" status.


In your Google Cloud Platform account navigate to the relevant project Resources tab. With the left side console menu select the dataset and schema you need.


You can also export your data to a different BI system. Also, you can schedule Auto-Export, Edit items, Delete, Share the data with other users, Clone the data source, Change the owner or service account key, and Set BQ Key on the Settings page.



Overall, BigQuery Jira integration can help businesses improve collaboration across teams, and achieve greater scalability and speed in their data analysis efforts. Alpha Serve's BigQuery Connector for Jira is a reliable and efficient way to integrate Jira data into BigQuery. It enables organizations to analyze their Jira data alongside other data sources and gain valuable insights for strategic decision-making.



Log in or Sign up to comment
Khrystyna Shparyk May 17, 2023

A nice article! Thanx! 

Maryna Pigol May 17, 2023

Great job!

AUG Leaders

Atlassian Community Events