Azure Data Factory vs Apache Airflow: A Head-to-head comparison

Data integration and migration are becoming more and more crucial for businesses across all industries as more companies use big data and the cloud.

Users can now concentrate on the data while scheduling, monitoring, and controlling ETL/ELT pipelines with a single view thanks to the efficient solutions provided by Airflow and Azure Data Factory. 

What is Azure Data Factory and how does it work?

Azure Data Factory is Azure's cloud ETL tool for scale-out serverless data integration and data transformation. It offers a code-free UI for simple authoring and single-pane management.

In order to orchestrate and automate data transfer and transformation via the cloud, Microsoft offers Azure Data Factory, a cloud-based integration service that enables you to develop data-driven workflows. 

What is the mechanism?

Azure Data Factory has the capability to connect to all the data and processing sources you'll need, in addition to SaaS applications, file sharing, and other web services.

With the help of the Data Factory service, it is possible to create data pipelines that transmit data and schedule them to run at set intervals. This demonstrates that there is a choice among employing a scheduled pipeline mode as well as a one-time pipeline mode.

What is Apache Airflow and what are its main services?

Apache Airflow is a platform for batch-oriented workflow development, planning, and monitoring that is open-source. You may create workflows integrating with almost any technology using the flexible Python framework provided by Airflow. 

Using Apache Airflow's workflow engine, your intricate data pipelines can be easily scheduled and carried out. It will make sure that every activity in your data pipeline is finished on time, with enough resources, and in the correct sequence. 

Is it possible to replace Azure Data Factory with Apache Airflow?

Azure Data Factory can be replaced with Apache Airflow.An Airflow workflow is composed of directed acyclic graphs, or DAGs, which are defined by Python code.

With Airflow's cutting-edge user interface, it's easy to observe currently-used pipelines, monitor their development, and address problems as they come up.

What are the key features that Azure Data Factory and Apache Airflow offer?

The following techniques can be used by Azure Data Factory to address these issues with moving data to or from the cloud:


Azure Data Factory

Control Flow

Although certain services, such as Azure Scheduler, Azure Automation, SQL VM, etc., are available for data transfer, Azure Data Factory's task scheduling features are superior to them.


Large amounts of data can be handled by Azure Data Factory thanks to its design.


Azure Data Factory always performs automatic encryption on all data in transit between the cloud and on-premises.


Apache Airflow

DAG File

Collection of DAG files that the scheduler and executor can view.

Web Server

It offers a convenient user interface for inspecting, triggering, and debugging DAG jobs and behavior.


It deals with starting workflows on schedules and sending tasks to the executor for execution.

How does Azure Data Factory compare to Apache Airflow in terms of pricing?

The cost of the data pipeline offered by Azure Data Factory is determined by the number of pipeline orchestration runs, the number of compute hours required for flow execution and debugging, and the number of Data Factory operations, such as pipeline monitoring.

The Apache License 2.0 governs Airflow, which is free and open source. There are no upfront restrictions or minimum fees.

You are charged for both the time that your Airflow Environment is running and any additional auto-scaling that is necessary to increase the number of workers or web servers.

Azure Data Factory vs Apache Airflow: Pros and Cons of each tool

1. Azure Data Factory Pros and Cons


  • No code Data Pipelines

    Data acquisition and integration from the most well-liked data sources, such as file systems, cloud storage services, and databases, may be accomplished with Azure Data Factory.

  • Simple SSIS Data Pipeline

    Migration of SSIS is simple. How easily SSIS data pipelines can be pulled and transferred to Azure Data Factory is one of the main benefits of Azure Data Factory for enterprises. 


  • Long-term costs

    While consumption-based pricing has many benefits, it may have a longer total cost of ownership than on-premises solutions.

  • Data Collector

    Custom code must be written in order to establish unique data sources, even if Azure Data Factory allows you to build data pipelines based on widely used sources like well-known databases and cloud storage providers.

2. Apache Airflow Pros and Cons


  • UI

    Due to its incredible user interface, Apache Airflow allows you to perform a variety of tasks, like checking run timings and logs, re-running processes, and monitoring the state of your DAG.

  • Open Source Platform

    Apache Airflow is a very active open-source project with a sizable and vibrant community. You can hunt up a solution to your issues online, study the source code to understand how it functions, and contribute to Airflow.


  • Customized Environment

    A team with experience in maintaining and enhancing an application that acts as the basis for all data-related tasks within a corporation is required for this.

  • One time Setup charge

    Regardless of the quantity of jobs, a one-time setup fee is fixed. New charges are determined by additional pipelines, how frequently they are executed, and the resources required to execute them.

How to choose wisely between Azure Data Factory and Apache Airflow for ETL?

Azure Data Factory

Apache Airflow


It offers a wide variety of transformation functions and supports both pre- and post-transformations.

Power QueryOnline or the GUI can be used to apply transformations without the need for coding.

A topological illustration called a DAG demonstrates how data moves within a system.

Job failures, retirements, and alarms are supported, and Apache Airflow maintains the execution dependencies between jobs in a DAG. 

Support, information, and instruction

ADF offers online forums and a help request form. It provides authentic, thorough documentation.

Customers can also get in touch with you by phone or email. Additionally, it provides printable training materials in digital format.

The documentation for Apache Airflow contains a fast start and how-to manual.

Additionally, it offers assistance to the Slack community. On its main page, it also offers some tutorials.

Sources and destinations of data through connectors

There are around 80 data sources that Azure Data Factory (ADF) can link with, including SaaS platforms, SQL and NoSQL databases, general protocols, and a variety of file kinds. 

Tasks, which are collections of actions, can be executed using operators, templates for tasks that Python functions or scripts can produce.


The greatest features of both tools can be utilized by combining ADF and Airflow. Airflow DAG allows for the execution of ADF jobs and extends the scope of Airflow orchestration beyond the ADF.

As a result, companies may build their jobs using ADF without difficulty and use Airflow as the control plane for the orchestration.

Hooks and Operators, which are capable of simple interaction and ADF pipeline execution, are the fundamental components of Airflow.


Why use Azure Data Factory instead of SSIS?

When compared to SSIS, Azure Data Factory provides both batch and streaming data operations.

Azure Data Factory enables you to specify a sequence of data-related actions that must be carried out, such as copying data across locations, analyzing it, and saving it in a database.

Can we write code in Azure Data Factory?

With the modifications made to the Data Factory V2 and Synapse pipeline Custom Activities, you may now build your own custom code logic in your choice language and run it on any supported Windows or Linux operating system through Azure Batch.

Can you run a Python script in Azure Data Factory?

Yes, you can upload and run the python script into Azure Data Factory.

What language is used in Azure Data Factory?

Data Factory offers a comprehensive collection of SDKs that you can use to create, administer, or monitor pipelines using your preferred IDE if you're an expert user seeking a programmatic interface. .NET, PowerShell, Python, and REST are among the languages supported.

About the author


Youssef is a Senior Cloud Consultant & Founder of

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