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:
- 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.
- Data Transformation Activities – These activities are related to data processing and extracting information from data. Dataflow Activity is an example of Data Transformation Activity.
- 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
Azure Data Engineering 
10 thoughts on “Azure Data Factory Pipelines and Activities”