When one works in a smaller company, there is an expectation of wearing many hats. As the Data Operations Team Lead, I’m no different. In addition to working with SQL Server, Azure infrastructure, Azure Data Factory and more, I have started to tackle Azure Dev Ops. Specifically, I’m working to enhance, or streamline, existing build and release pipelines to work better in our organization. Learning how pipelines are built and behave, while re-vamping the process has led to some deeper dives into YAML and what Azure Dev Ops is doing.
Some of the developers on our team are relatively new to developing code in a corporate environment and using source control. To help reduce issues around check-ins and code merging, we have made some conservative decisions. The data warehouse solution consists of 3 database projects, Staging, Warehouse and SystemLogging, as well as Azure Data Factory artifacts. Each solution is stored in its own repository within the same Azure Dev Ops project. The build and release pipeline are within a proof of concept project, AutomationTestingPoC.
In this series of posts, I’ll document some of the nuances found in Azure Dev Ops and try to explain their benefits and drawbacks. The links below are listed chronologically, as I’m documenting them.
- Jobs versus Tasks
- Azure Data Factory Deployment Mistake – Task scope vs ARM template
- Staging specific build artifacts with PowerShell
Check back often, as the list will grow and topics will be linked.
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