Using tools such as BigQuery for organizations dealing with big data is essential, primarily if you use Jira as your project and issue-tracking software.
BigQuery is a REST-based enterprise data warehouse that lets you run complex queries on big data.
Jira Reporting is limited, especially when dealing with large data sets. You’ll need to integrate Jira to BigQuery to do top-notch data analysis to solve this.
This article explores Jira BigQuery integration for complex data analysis, highlighting the benefits, analyzing key features of BigQuery Connector for Jira, and providing a step-by-step guide on how to connect BigQuery to Jira.
Benefits Of Jira BigQuery Integration
What is BigQuery? BigQuery is a fully managed cloud service that you can use to collect and analyze big data.
It helps organizations such as logistics to make data-driven decisions quickly. Moreover, it also lets you store massive data sets and run complex yet superfast SQL queries.
On the other hand, Jira is a project management system for teams to communicate, collaborate and build things.
Depending on your business size, you might generate a lot of data! And that’s where BigQuery Jira integration can bring in multiple benefits. Let’s explore them below.
Advanced Jira Data Analysis
Once you integrate Jira with BigQuery, you can benefit from advanced data analysis.
BigQuery lets you do plenty of advanced data analysis, including descriptive and predictive analysis. You can do machine learning, geospatial, ad hoc, and business intelligence analytics here.
You can also carry out real-time analytics. Here, you can set up streaming data pipelines to analyze large datasets.
BigQuery BI engine is used to analyze those datasets while offering excellent in-memory analysis service for faster processing.
Improved Data Storage And Management
BigQuery also offers improved data storage and management. Here, you can store and effectively manage unlimited Jira data.
The BigQuery storage is optimized for query performance, especially for large datasets. Also, it offers excellent data management as it separates storage and computing to improve scaling.
Enriched Data Sets
BigQuery enriches your data sets, turning diverse, unstructured Jira data into a structured, clean format ready for analysis.
This allows you to gain comprehensive insights, offering a holistic view of your business.
With this broader perspective, you can make informed, action-based decisions that consider the entirety of your operations, not just isolated aspects.
Advanced Machine Learning Capabilities
BigQuery offers access to built-in machine learning (ML) capabilities. As a data analyst, you can use BigQuery to execute machine learning models with SQL queries without any ML-specific knowledge or programming skills.
The ML capabilities can work with large datasets containing structured, semi-structured, and unstructured data. Furthermore, you can also export the BigQuery ML models to your serving layer, such as Vertex AI.
Also, you can access the ML models through Google Cloud Console, BigQuery REST API, bq command-line tool, or using a third-party external devices.
Faster Data Processing And Query Performance
BigQuery boasts BI Engine, an impressive engine that delivers rapid data processing and exceptional query performance, achieving remarkable speeds of reading 1 TB of data through 330 x 100 MB/sec dedicated hard drives, supported by a robust 330 Gigabit network.
Integrating Jira With BigQuery Using The BigQuery Connector For Jira
There’re several ways to integrate Jira with BigQuery. But, the best way to connect them is to use BigQuery Connector for Jira.
It is the only no-code enterprise-grade app for BigQuery Jira integration. Using it, you can automate loading Jira data to BigQuery, and focus on what’s essential, for example, data management and analysis.
5 Key Features Of Jira BigQuery Connector
The Jira BigQuery Connector offers a range of features to facilitate seamless data integration and analysis. Here are some of the key features:
1.) User-Friendly, No-Code Solution
The Jira BigQuery Connector is designed to be user-friendly, requiring no technical expertise to install, configure, or use.
The setup process is straightforward, involving only three simple steps. This plug-in is designed to be accessible to all users, regardless of their technical proficiency.
2.) Unlimited Jira Data Import & Multiple Data Sources
This connector allows for the limitless import of Jira data, including custom fields and add-on data, thereby ensuring comprehensive data analysis.
Once installed, you can automatically import an unlimited amount of data from Jira and create as many data sources as required. This feature provides you with extensive flexibility in managing and analyzing your data.
3.) Safety And Permission Control
The Jira BigQuery Connector ensures the safety of your data. You can set up specific permissions to control who can access the data.
This feature allows you to make data available to relevant Jira users or groups while maintaining data security.
You can apply these permissions across multiple data sources, providing comprehensive control over your Jira data.
4.) Advanced Filtering Options
The Jira BigQuery Connector offers robust filtering options, allowing you to extract precisely the data you need for your analysis.
You can use basic filters and Jira Query Language (JQL) to narrow down your data. For more granular control, advanced filters are available, enabling you to fine-tune the data transfer between Jira and BigQuery.
5.) Support For Calculated Fields
The connector supports calculated fields such as Time at Assignee, Time in Status, and Time at Current Assignee.
By importing these calculated fields, you can gain deeper insights into your team’s performance.
This feature allows you to measure and analyze specific aspects of your team’s work, thereby facilitating data-driven decision making.
Step-by-Step Guide On Setting Up BigQuery Connector For Jira
This section will take a closer look at how to connect Jira to BigQuery using BigQuery Connector for Jira.
Step 1: Installing The BigQuery Connector For Jira
To install the plugin, you need Jira administrative rights.
You can conveniently install the BigQuery Connector for Jira directly from the Atlassian Marketplace by adhering to the standard installation process.
Alternatively, you can also install it directly from your Jira Cloud instance. Simply navigate to the Apps section in Jira Cloud, then proceed by clicking on the Explore more apps section to find and install the BigQuery Connector for Jira.
In the search field, enter “BigQuery Connector for Jira” Press the “Enter” button to find the app listed.
Click “Try it free” to start your 30-day free trial license. It’ll start the installation process, and once it is done, you’ll receive the “successful installation” message.
Step 2: Assigning Permissions For Utilizing The BigQuery Connector For Jira
You need to set permissions to facilitate the users to work with the connector. Only an administrator, by default, can use the BigQuery Connector for Jira. He can also set permissions for others.
Go to the Jira Cloud Main Menu navigation and choose Apps then follow BigQuery Connector for Jira to access it. Open BigQuery Connector for Jira, from the left-side navigation menu, select “Administrator.”
Next, you’ll find different options on the plugin’s Settings page. For example, you can:
- Overview Granted permissions (1).
- Edit permissions using the “Edit” icon, including adding or removing users/groups (2).
- Remove users from using the connector with the “Remove” icon (3).
Within the Edit permissions window, select the desired groups by clicking on Select groups.
Similarly, identify and select the users you want to grant permissions to by typing in their usernames in the Select users field. Once all the necessary groups and users are selected, finalize the permissions by clicking Save.
Step 3: Creating Jira API token
To ensure seamless export of specific Jira tables — including User Profiles, Sprint Reports, and Velocity Charts — to BigQuery, the creation of a Jira API token is necessary.
Navigate to Account settings and select the Create and manage API tokens link within the Security tab.
Then, initiate the creation of a new token by clicking on Create API token, input a suitable name for the token, and hit Create.
Remember to copy your newly created API token as it won’t be visible again. Finally, input the token into the designated field at Apps > BigQuery Connector for Jira > Tokens > Jira API token and confirm by clicking Validate & Save.
Step 4: Creating Data Source In BigQuery Connector For Jira
Begin by accessing the BigQuery Connector for Jira app. Proceed by clicking the Create a Data Source button and fill in the required fields such as Name, Dataset name, and Description.
To manage sharing preferences, select the Share Settings button and choose the Groups and/or Users you wish to share your Data Source with. Once you’ve made your selections, click Save.
For those seeking more control over their data selection, advanced filtering options are available.
In the Filter issues section, you can manage your data selection in a few ways:
- To export all existing issues, simply select All.
- For a custom request, opt for Select by JQL. JQL, or Jira Query Language, is a flexible search tool in Jira. Input your JQL expression and hit Submit JQL to apply.
- For standard field filters, select Basic. Click Issue filter to set it up. You can select from available Projects, Issue Types, Statuses, and date ranges for Created and Updated issues. Click Apply to set the filter.
Once you’ve applied the necessary filters, proceed to select the specific Jira fields you wish to export to BigQuery.
To streamline this process, you can enter the name of the desired field into the Search bar.
However, if you prefer a hands-on approach, you’re free to select the fields manually. Simply scroll through the list of available fields and check the box adjacent to the ones that meet your requirements.
Once you’ve made your selections click Save button to create your data source, Preview to review the specifics of your data source, or Preview ERD to visualize the Entity Relationship Diagram based on your selections.
Step 5: Creating Google Cloud Service Account Key
To bring data into BigQuery, first, you must create and set up a Google Cloud service account key. To create a Google Cloud service account key, follow these streamlined steps:
- Log in to Google Cloud Platform. If you don’t already have one, create a project.
- Enable the IAM API by confirming your project and then enabling the API.
- Navigate to the Home page console, select the APIs & Services tab, and click Credentials.
- Choose Create Credentials and select Service account.
- Follow the service account creation process:
- For Service account details, an email address will be auto-generated. Copy it and click Create and continue.
- Grant project access to this service account and click Continue.
- Grant user access to this service account and click Done. Ensure the associated email address is linked to an active Google account.
- After creating the service account, click Manage service account link. On the project’s service account page, click Actions, select Manage keys.
- Click ADD KEY, select Create new key. Choose JSON as key type and click Create. The key will auto-download.
Proceed to configure the service account key in the BigQuery Connector for Jira. Launch the app, and adjacent to your newly created Data Source, click the Export data button. From there, choose your service account key .json file, and hit Submit.
Upon successful upload of your service account key, a confirmation message will be displayed.
Step 6: Loading Jira Data Into Google BigQuery
To begin the data export process, click the Export Data button. The data export will be initiated until it reaches the NOT EXPORTED status, indicating the export is complete.
Now go to your Google Cloud Platform account. Select the project in which you created the service account key.
From there, go to the Resources tab.
Go to the left-side console menu, and open all project datasets (1). Then choose a particular dataset (2) and schema (3). Finally, edit schema (4).
That’s it! Your Jira data has been successfully loaded into BigQuery. You’re now free to manipulate and analyze the data as per your requirements.
More Options For Loading Jira Data Into BigQuery
You can also use more options to upload Jira data to BigQuery. These include:
Custom Scripting Using APIs
You can use REST APIs to connect BigQuery to Jira if you’re technical. It lets you connect numerous different endpoints.
The scripts work great for most cases but have limitations such as no real-time data access, associated infrastructure, and technical maintenance.
Third-party ETL Tools
Businesses can also use third-party ETL tools to load Jira data into BigQuery. These ETL tools create a pipeline that loads and enriches the data. Moreover, you get real-time data for analysis.
Google Cloud Dataflow
You can also use Google Cloud Dataflow to create a unified stream between Jira and BigQuery.
It is a large-scale data processing service that lets you horizontally and vertically scale resources for automated data provisioning and management.
Combining Dataflow with BigQuery allows you to create an excellent workflow that offers scalable and optimized connectivity.
Conclusion
Connecting Jira to BigQuery is not a puzzle. It requires carefully setting up the tools to offer a seamless yet automated data transfer.
There are multiple ways to do so. However, the best way is to use BigQuery Connector for Jira, a no-code approach that helps you automate data transfer.