azure-data-factory

Just because you can doesn’t always mean you should

Reference architectures are great!

Azure Data Factory - Production deployment

After publishing the first few posts in my series on CI/CD for Azure Data Factory, I got stuck into the next installment, released yesterday, on how to handle Integration runtimes (which argueably should have come first🤫).

This is part of a series of blog posts where I’ll build out Continuous Integration and Delivery (CI/CD) pipelines using Azure DevOps, to test, document, and deploy Azure Data Factory.

Azure Data Factory - Delivery components

This is the first in a series of blog posts where I’ll walk through the steps to build out Continuous Integration and Delivery (CI/CD) pipelines using Azure DevOps, to test, document, and deploy an Azure Data Factory.

This week saw the end of Hacktoberfest, so I hope you managed to get your four pull requests in before Sunday (assuming you were participating).

Whether you are changing naming conventions, migrating to a new tenant or handing over development resources to a customer, there are several scenarios where you may want or need to create a new data factory to develop in, or change the source of your data factory content to a new repo.

I’m continuing the theme of #Hacktoberfest this week with another link on contributing to open source down the bottom.