Jira to Databricks Integration: 2 Easy Methods to Connect

You are going about your day setting up and operating your organization’s data infrastructure and preparing it for further analysis. Suddenly, you get a request from one of your team members to replicate data from Jira to Databricks. 

We are here to help you out with this replication process. You can transfer data from Jira to Databricks using REST API or pick an automated tool to do the heavy lifting for you. This article provides a step-by-step guide for both of them.

How to Connect Jira to Databricks?

The first method of replication is using REST API.

Export Jira to Databricks using REST API

Getting Data from Jira

Using Jira’s REST API, which provides access to issues, comments, and numerous more endpoints, you can extract your data from the software. For example, to get data about an issue, you could call:

GET /rest/api/2/issue/[issueIdOrKey]

Sample the Data

Jira’s API returns data in JSON format. Here is an example of the endpoint’s response to an issue.

{
    "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",
        }
    ]
}

Prepare the Data

The data fields must be mapped into a schema that can be inserted into your database once you have the JSON data in hand. This implies that you must choose a specified datatype (such as INTEGER, DATETIME, etc.) and create a table that can store each item in the response.

Load the Data into Databrick’s Delta Lake

You can modify existing Apache Spark SQL code to convert Parquet, CSV, or JSON formats into delta formats to construct a delta table. When you have a delta table, you can use Apache Spark’s Structured Streaming API to write data into it. Even when additional streams or batch queries are being executed against the table simultaneously, the delta lake transaction log ensures processing will only occur exactly once. Streams operate by default in append mode, which updates the table with new records. 

Using REST API to replicate data from Jira to Databricks is ideal in the following situations:

• One-Time Data Replication: When your business teams require these Jira files quarterly, annually, or for a single occasion, manual effort and time are justified.
• No Transformation of Data Required: This strategy offers limited data transformation options. Therefore, it is ideal if the data in your spreadsheets is accurate, standardized, and presented in a suitable format for analysis.
• Lesser Number of Files: Downloading and composing SQL queries to upload multiple CSV files is time-consuming. It can be particularly time-consuming if you need to generate a 360-degree view of the business and merge spreadsheets containing data from multiple departments across the organization.

You face a challenge when your business teams require fresh data from multiple reports every few hours. For them to make sense of this data in various formats, it must be cleaned and standardized. This eventually causes you to devote substantial engineering bandwidth to add new data connectors. To ensure a replication with zero data loss, you must monitor any changes to these connectors and fix data pipelines on an ad hoc basis. These additional tasks consume forty to fifty percent of the time you could have spent on your primary engineering objectives.

How about you focus on more productive tasks than repeatedly writing custom ETL scripts, downloading, cleaning, and uploading CSV files? This sounds good, right?

In that case, you can…

Automate the Data Replication process using a No-Code Tool

Going all the way to use CSV files for every new data connector request is not the most efficient and economical solution. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare.

You can streamline the Jira to Databricks data replication process by opting for an automated tool. To name a few benefits, you can check out the following:

• It allows you to focus on core engineering objectives. At the same time, your business teams can jump on to reporting without any delays or data dependency on you.
• Your marketers can effortlessly enrich, filter, aggregate, and segment raw Jira data with just a few clicks.
• The beginner-friendly UI saves the engineering team hours of productive time lost due to tedious data preparation tasks.
• Without coding knowledge, your analysts can seamlessly aggregate campaign data from multiple sources for faster analysis.
• Your business teams get to work with near real-time data with no compromise on the accuracy & consistency of the analysis.

As a hands-on example, you can check out how Hevo Data, a cloud-based no-code ETL/ELT tool, makes the Jira to Databricks data replication effortless in just 2 simple steps:

Step 1: Configure Jira as a Source

Step 2: Configure Databricks as a Destination

That’s it, literally! You have connected Jira to Databricks in just 2 steps. These were just the inputs required from your end. Now, everything will be taken care of by Hevo Data. It will automatically replicate new and updated data from Jira to Databricks every 1 hour (by default). However, you can also increase the pipeline frequency as per your requirements.

You can also visit the official documentation of Hevo Data for Jira as a source and Databricks as a destination to have in-depth knowledge about the process.

Hevo Data’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. It also enriches the data and transforms it into an analysis-ready form without writing a single line of code.

Hevo Data’s reliable data pipeline platform enables you to set up no-code and zero-maintenance data pipelines that just work. By employing Hevo Data to simplify your data integration needs, you can leverage its salient features:

• Fully Managed: You don’t need to dedicate any time to building your pipelines. With Hevo Data’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
• Data Transformation: Hevo Data provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
• Faster Insight Generation: Hevo Data offers near real-time data replication, giving you access to real-time insight generation and faster decision-making. 
• Schema Management: With Hevo Data’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
• Scalable Infrastructure: With the increased number of sources and volume of data, Hevo Data can automatically scale horizontally, handling millions of records per minute with minimal latency.
• Transparent pricing: You can select your pricing plan based on your requirements. Different plans are put together on its website and all the features it supports. You can adjust your credit limits and spend notifications for increased data flow.
• Live Support: The support team is available round the clock to extend exceptional customer support through chat, email, and support calls.

Get started for Free with Hevo Data!

Learn how to load data from Jira to Redshift.

What can you hope to achieve by replicating data from Jira to Databricks?

Here are a few things you can achieve by replicating data from Jira to Databricks:

• Boost Server Performance: Data replication can improve and even accelerate server performance. Users can get data significantly faster when organizations operate multiple data copies on several servers. Furthermore, by routing all data read activities to a replica, administrators free up processing cycles on the original server for more resource-intensive writing operations.
• Disaster Relief: The biggest advantage is in disaster relief and data security. It guarantees that a consistent backup is kept in the case of a disaster, hardware failure, or system breach that might endanger data. So, if a system fails for any of the reasons listed above, you may access the data from a new place.
• Help with Data Analytics: Data-driven enterprises typically replicate data from several sources into data repositories such as data warehouses or data lakes. This makes it easier for the analytics team, which is distributed across many teams, to work on joint projects.

Key Takeaways

If data replication must occur every few hours, you will have to switch to a custom data pipeline. This is crucial for marketers, as they require continuous updates on the ROI of their marketing campaigns and channels. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+data source (including 40+ free sources such as Jira).

Databrick’s “serverless” architecture prioritizes scalability and query speed and enables you to scale and conduct ad hoc analyses much more quickly than with cloud-based server structures. The cherry on top — Hevo Data will make it further simpler by making the data replication process very fast!

Visit our Website to Explore Hevo Data

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

Want to take Hevo Data for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.