JIRA to BigQuery: 2 Methods to Move Data Instantly

Unlock the full potential of your JIRA data by integrating it seamlessly with BigQuery. With Hevo’s automated pipeline, get data flowing effortlessly—watch our 1-minute demo below to see it in action!

Without the right architecture, storing and querying large amounts of data can be time-consuming and expensive. So, you need a data warehouse like BigQuery to solve this problem. BigQuery uses Google’s processing power, which enables super-fast SQL queries. In this blog, we are going to discuss two popular methods of transferring data from JIRA to BigQuery for deeper analysis. Before we get into that, let’s understand these two applications.

Why You Should Integrate Jira with BigQuery?

Jira BigQuery integration has the following benefits:

• Scalability: BigQuery is designed to handle enormous amounts of data, allowing you to accommodate your growing data needs.
• Real-time data synchronization: Load your Jira data directly to bigQuery in real-time for faster analysis. 
• Advanced analytics: BigQuery offers SQL-based querying and machine learning capabilities that help in advanced analysis of your data. 
• Comprehensive reports and dashboards: After loading data to BigQuery, you can connect this to your visualization tools for creating comprehensive reports and dashboards. 

Prerequisites

Here is a list of prerequisites to get you started with the data migration:

• JIRA account with admin access.
• GCP (Google Cloud Platform) account with billing enabled.
• Google Cloud SDK is installed on your CLI.
• BigQuery API enabled.
• BigQuery.admin permissions.

Methods to connect JIRA to BigQuery

There are multiple methods that can be used to connect JIRA to BigQuery and load data easily:

• Method 1: Using Hevo Data, a No-code Data Pipeline
• Method 2: Using custom ETL scripts to load data from JIRA to BigQuery

Method 1: Using Hevo Data, a No-code Data Pipeline

Hevo Data focuses on two simple steps to get you started:

• Configure Source: Connect Hevo Data with JIRA by providing the API Key and User Email for your authorised Atlassian/Jira account, along with your Atlassian Site Name and a unique name for your pipeline.

• Integrate Data: Load data from JIRA to BigQuery by providing your BigQuery database credentials. Enter the authorised account associated with your data and the ID of the project, whose data you want to transfer, along with a name for your dataset to connect in a matter of minutes.

Method 2: Using custom ETL scripts to load data from JIRA to BigQuery

The steps involved in moving data from JIRA to BigQuery manually are as follows:

Step 1: Extract

Extracting Data from JIRA using REST APIs with a Python Script

import requests
response = requests.get("https://your-domain.atlassian.net/rest/api/2/issue/ISSUE-123/comment", auth=('email', 'api_token'))
data = response.json()

Step 2: Transform

Convert the JSON response to CSV or NDJSON for BigQuery.

import json
with open('jira_comments.ndjson', 'w') as f:
    for comment in data['comments']:
        f.write(json.dumps(comment) + '\n')

Step 3: Load

Load: Use the bq CLI to load the file into BigQuery.

bq load --source_format=NEWLINE_DELIMITED_JSON your_dataset.jira_comments jira_comments.ndjson schema.json 

Limitations of using custom ETL scripts to connect JIRA to BigQuery

• Real-time data sync is resource-intensive and hard to manage efficiently.
• Requires ongoing maintenance as API changes can break the data pipeline.
• Demands strong programming skills and robust error handling.
• Doesn’t support automatic schema updates, making maintenance harder.

Conclusion

In this article, you learned how to effectively transfer data from JIRA to BigQuery using two different techniques. You can manually write custom scripts to extract the data from Jira using Rest APIs, prepare the data by creating a schema, and then finally load it into BigQuery. Regularly extracting and loading the data manually can be a time-consuming task. You would have to invest a section of your engineering bandwidth to integrate, Clean, Transform, and load your data to your desired destination so that you can perform business analysis in real-time. All of this can be easily solved by a Cloud-Based ETL Tool like Hevo Data.

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