Azure Data Factory Pipelines and Activities

In previous post, we have discussed about Azure Data Factory in brief. This post will be a discussion about pipelines and activities that can be performed using Azure Data Factory.

A pipeline is a grouping of activities that are arranged to accomplish a task together. Pipelines help users to manage the individual activities as a group and provide a quick and easy overview of the activities involved in a complex task with multiple steps.

Below is Microsoft’s illustration to understand the relationship between activities and pipelines better:

There are a lot of different activities available in Azure Data Factory. Some of the most widely used ones are:

Copy Activity: Used to import data from SQL Server to Azure

Dataflow Activity: To process and transform data using Azure Services such as Synapse Analytics

Azure Data Factory activities can be grouped into three parts:

  1. Data Movement Activities – These activities are used for ingesting data into Azure or exporting data from Azure to external data stores. Copy activity is an example of Data Movement Activity.
  2. Data Transformation Activities – These activities are related to data processing and extracting information from data. Dataflow Activity is an example of Data Transformation Activity.
  3. Control Activities – Activities that specify a condition or affect the progress of the pipeline. E.g. For Each activity is used for repeating control flow, Wait Activity induces a delay in the pipeline execution.  

There are a lot of other activities available in Azure Data Factory. For a complete and current list of supported activities please go to: https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipelines-activities

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