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Create First Azure Data Factory Pipeline


In my previous post, we explored the basics of creating First Azure Data Factory. Today, I want to delve deeper into the process of creating and configuring pipelines within the Azure Data Factory.

Azure Data Factory Pipeline

A pipeline in Azure Data Factory (ADF) represents a set of logically connected activities. It enables the efficient and reliable flow of data from a source to a destination. A pipeline is similar to SQL Server Integration Services (SSIS) packages. It allows you to organize and execute a series of related tasks. These tasks are in a specific order to complete a larger process. Each pipeline consist of activities that perform specific actions like copying or transforming data, running scripts, and more.

By connecting these activities in a particular sequence, you can create a data flow that moves and transforms data from a source to a destination. In ADF, pipelines are highly customizable and can be parameterized, scheduled, and monitored to ensure successful execution of your data integration process. Here are the steps to create a pipeline in Azure Data Factory.

  1. After creating the First Azure Data Factory, lunch it.

  1. Once you connected to Azure data factory studio successfully, click on the “Author” button to use the factory resources.
  2. In the authoring environment, click on the “+” sign to create “New Pipeline” on the left-hand side of the screen.
  3. Give your pipeline a name and click on “Create” to create your pipeline.

Azure Data Factory uses pipelines to perform a specific data integration task through interconnected activities.

  • Parameters: It is possible for users to pass values into a pipeline using Parameters. This allows for dynamic configuration of the behavior of the activities within the pipeline.
  • Variables: Activities in a pipeline can create and modify variables. These variables can store and manipulate data values throughout the pipeline.
  • Settings: Pipeline settings define various configuration options for the pipeline, such as retry behavior, timeout values, and logging settings.
  • Output: A pipeline generates an output after completing its data integration task. This output can be sent to various destinations for further analysis or processing, such as storage accounts, databases, or data lakes.

Wishing you an enjoyable learning experience!

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