Azure Data Factory for Beginners - Build Data Ingestion - Publishing the Data Factory to Azure DevOps

This video tutorial guides viewers through the process of cleaning up Azure Data Factory pipelines, publishing changes to Azure DevOps, and configuring a code repository. It emphasizes the importance of version control and proper branching practices. The tutorial also explains how Data Factory stores objects in JSON format and demonstrates navigating through the repository to confirm file uploads.

What is the first step in cleaning up the data factory pipelines?

• Validate all pipelines

• Open the data factory and select author

• Select the publish button

• Create a new pipeline

Which branch should be selected as the collaboration branch when setting up the repository?

• Release

• Development

• Feature

• Main

What is the purpose of the ADF_publish branch?

• To store backup data

• To develop new features

• To test code

• To manage releases

Why is it not advisable to work directly on the main branch?

• It is only for administrators

• It is used for storing old versions

• It is intended for production deployment

• It is reserved for testing

What format does the data factory use to store its objects?

• XML

• CSV

• JSON

• YAML

Which component is NOT mentioned as being stored in JSON format?

• Scripts

• Linked services

• User accounts

• Datasets

What should be done after confirming the changes in the Azure DevOps repository?

• Navigate back to the project

• Create a new branch

• Revert all changes

• Delete the repository