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JIRA Data in Azure Data Lake

Our BI team is looking to use Power BI to report against Jira Data. The analysis we have done leads to the conclusion that utilizing the JIRA API’s will not be sufficient.

We have looked at several plugins that utilize APIs and each requires connections to be limited to a small scope, like specific projects, teams, issue types, etc. Our reports will be on an enterprise level and being limited like that calls for many connectors, resulting in excess time to render and high processing costs.

We are currently working with a consultant that has worked extensively with other companies that utilize Power BI to report against JIRA data. He has indicated that they have had more success by connecting to the database directly, rather than relying on APIs.

To that end, we would like to explore putting our JIRA data into an Azure Data Lake.

Does anyone have any whitepapers, best practices, etc.  that they would be willing to share?

Thank you in advance.

2 answers

Hi @Scott Derderian !

If you are still looking for solutions, you might check out eazyBI

eazyBI has integration with Jira default fields as well as the most popular Jira app fields. With eazyBI, you can do extensive Jira and additional data analysis as well as build custom reports.

You can see report examples in the demo account and read more in eazyBI documentation

Please let me know if you have further questions regarding eazyBI!
Lauma /

0 votes
Brant Schroeder Community Leader Sep 23, 2021

@Scott Derderian 

We pull data out of Jira for reporting and do it directly from the DB. I would not recommend it as it takes a lot of overhead and you will constantly need to be aware of any DB scheme changes.  Since you are using PowerBI use the connector.  Then all the work is done for you.

If you do decide to do it, I would suggest the following:

  • Ensure that the account accessing the DB is read-only.  If you change something you could break your instance.  You might also consider snaping the DB and pulling from the snap.
  • Know the data that you need and how it relates to each other.  If you do not understand the data you might get some odd results.  Make sure you study the DB scheme and know it well. 
  • Have a test instance so you can validate updates to ensure your data syncs will not be impacted.

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