Azure DevOps
by Microsoft
Azure DevOps spans the whole delivery loop: Boards work items with their states and iterations, Repos with pull requests and reviewers, Pipelines builds and releases per environment, and Test Plans runs. Joining those areas in your own Lake gives engineering leaders one place to measure cycle time, review load and release health, and to set them against incidents, support cases and cost.
- Category
- Developer Tools
- Type
- Code hosting
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- OAuth 2.0, Access token, Username and password
02Data
What you can sync.
- Work items
- Repositories
- Pull requests
- Builds
- Releases
- Test runs
Report on Azure DevOps work items in DataPrism BI and let an AI Mind answer delivery questions in plain words.
Need more? Name any other Azure DevOps 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)
- An access token
- A username and password
Azure DevOps uses OAuth 2.0 (client ID, client secret, refresh token), an access token or a username and password. You add the credentials once, when the AI asks for them, and it tells you where to create them in Microsoft if you need help.
04Questions
Questions you can answer.
Once Azure DevOps 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 Active to Closed for bugs and user stories, by area path and iteration?
- How long do pull requests wait for a first review, and which reviewers carry more than their share of reviews?
- What share of builds fail per pipeline and branch, and how long do they take?
- How many releases reach production per month, and how many deployments to production fail?
- Which test cases fail across several test runs in a row?
05How it works
How it works in DataMind.
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Pick Azure DevOps.
Open Lake, pick Azure DevOps in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the Azure DevOps API docs.
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Answer a few questions.
The AI reads the Azure DevOps documentation, then asks which credentials you will use, which data to sync (for example work items and repositories), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
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Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then Azure DevOps data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Azure DevOps.
Sources teams join with Azure DevOps in the Warehouse, each one connected the same way.
