Jira
by Atlassian
Jira tracks how software work is planned and done: issues with types, priorities and story points, projects, boards and sprints, worklogs of time spent, and the changelog of every status change. That changelog is the valuable part, because once it sits in your Lake it gives exact cycle times, sprint carry-over and reopen rates that can be joined to code, support and customer data.
- Category
- Developer Tools
- Type
- Issue tracking
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- OAuth 2.0, Username and password, Access token
- Docs
- Jira API docs
02Data
What you can sync.
- Issues
- Projects
- Sprints
- Boards
- Worklogs
- Changelogs
Join Jira issues with product usage and revenue to see what each release changed.
Need more? Name any other Jira data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- OAuth 2.0 (client ID, client secret, refresh token)
- A username and password
- An access token
Jira uses OAuth 2.0 (client ID, client secret, refresh token), a username and password or an access token. You add the credentials once, when the AI asks for them, and it tells you where to create them in Atlassian if you need help.
04Questions
Questions you can answer.
Once Jira data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- What is the cycle time from In Progress to Done by issue type and team, based on the changelog?
- How many story points were committed versus completed in each sprint, and what carried over?
- Which bugs were reopened after being resolved, and in which components?
- How do hours logged in worklogs compare with original estimates per epic?
- How many issues have sat in the backlog untouched for more than six months?
05How it works
How it works in DataMind.
-
Pick Jira.
Open Lake, pick Jira in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the Jira API docs.
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Answer a few questions.
The AI reads the Jira documentation, then asks which credentials you will use, which data to sync (for example issues and projects), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then Jira data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Jira.
Sources teams join with Jira in the Warehouse, each one connected the same way.
