Airflow vs Azure Data Factory: Guide to Choose the Right Tool

Managing and orchestrating data workflows efficiently is crucial in today’s data-driven world. As the amount of data constantly increases with each passing day, so does the complexity of the pipelines handling such data processes. Data orchestration deals specifically with the management and coordination around data pipelines to guarantee the free flow of data from one system to another. On the other hand, ETL is used to extract data from distinct sources, transform it into a workable format, and then load it into the target system.

The selection of the right tool for data orchestration is of utmost importance. It can make the difference between a well-functioning and efficient data operation and a potential disaster. This blog will compare two tools: Apache Airflow vs Azure Data Factory. By the end, you will clearly understand each tool’s strengths and weaknesses, enabling you to make an informed decision that aligns with your specific requirements. 

Apache Airflow Overview

G2 Rating: 4.3(86)

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow’s extensible Python framework allows you to build workflows linking almost any technology. Its web interface helps manage the state of your workflows. Airflow can be deployed in various ways, from a single process running on your laptop to a distributed setup running on several servers to handle even large workloads.

Key Features

1. Dynamic: Airflow pipelines are configured using Python code, allowing for dynamic pipeline generation.
2. Extensible: All Airflow components are extensible, allowing the community to extend them so that Airflow can integrate with most of the world’s services.
3. Flexible: Allows workflow parameterizing using the Jinja templating engine.
4. Directed Acyclic Graphs: Airflow uses DAGs to define workflows that organize tasks into a directed graph with dependencies. 

Common Use Cases of Airflow

1. Business Operations: Apache Airflow’s tool agonist and extensible quality make it a preferred solution for many business operations. 
2. ETL/ELT: Airflow allows you to schedule your DAGs in a data-driven way. It also uses Path API, simplifying interaction with storage systems such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. 
3. Infrastructure Management: Setup/teardown tasks are a particular type of task that can be used to manage the infrastructure needed to run other tasks.
4. MLOps: Airflow has built-in features that include simple features like automatic retries, complex dependencies, and branching logic, as well as the option to make pipelines dynamic.

Azure Data Factory Overview

G2 Rating: 4.6(77)

Azure Data Factory, or ADF, is Microsoft’s cloud-based data integration service. It is designed to facilitate the creation, scheduling, and orchestration of data pipelines in the cloud. ADF is a fully managed service where Microsoft provides infrastructural involvement while you focus on your data workflows.

Key Features

1. Easy rehosting of SQL Server Integration Services to build ETL and ELT pipelines code-free with built-in Git and support for continuous integration and delivery (CI/CD).
2. Pay-as-you-go, fully managed serverless cloud service that scales on demand for a cost-effective solution.
3. More than 90 built-in connectors for ingesting all your on-premises and software-as-a-service (SaaS) data to orchestrate and monitor at scale.

Common Use Cases of Azure Data Factory

1. Cloud-first environments: Those who make heavy investments in Azure or, for that matter, any cloud service find great comfort in the fact that ADF is integrated and scalable.
2. Simplified Workflows with GUI Requirements: The ADF GUI is suitable since most teams want to keep it under low or no code for building and maintaining data pipelines.
3. Large-Scale Data Movements: ADF is well-suited for big data movements in the cloud among heterogeneous sources and sinks using Azure services.
4. GitHub integration: ADF facilitates this collaboration by connecting to GitHub repositories, allowing for streamlined version control and collaborative development.

Airflow vs Azure Data Factory: Key Comparisons

Aspect	Airflow	Azure Data Factory
Ease of Use	Requires more technical expertise, steep learning curve.	User-friendly interface, easier for beginners.
Integration and Compatibility	It supports a wide range of data sources and has third-party solid integration.	Well-integrated with Azure services, good support for various data sources.
Scalability and Performance	Highly scalable, suitable for large and complex workflows.	Scales automatically with Azure, optimal for cloud environments.
Cost	Open-source, cost-effective, but requires infrastructure.	Pay-as-you-go pricing, integrated into Azure billing.
Security and Compliance	Offers security through plugins and custom setups.	Built-in security features and compliance with Azure standards.
Community and Support	Large open-source community with extensive resources.	Backed by Microsoft with robust support and documentation.

Head-to-Head Comparison

Ease of Use

• Airflow

Airflow requires technical knowledge of Python and the command line. Its learning curve is steep, especially for beginners. However, if you are comfortable with coding, Airflow’s flexibility and power are unparalleled.

• Azure Data Factory

ADF is more user-friendly, with a visual interface that reduces the need to code. This makes it accessible to a broader circle of people without a firm technical background.

Integration and Compatibility

• Airflow

Airflow is highly versatile, supporting a wide range of data sources and third-party integrations. It excels in environments where custom integrations are needed.

• Azure Data Factory

ADF also integrates really well with other Azure services, although it supports several external data sources as well. However, where it shines is in its native compatibility with the Azure ecosystem. 

Scalability and Performance

• Airflow

Airflow was designed for complex, large-scale workflows. The platform is highly scalable, but scaling normally requires manual tuning and management at an infrastructural level.

• Azure Data Factory

ADF automatically scales up and down based on your workload. It is generally a high-end choice in cloud environments where you must run large-scale data operations without worrying about infrastructure.

Cost

• Airflow

Being open-source, Airflow is cost-effective in terms of licensing. However, you need to consider the costs associated with managing the infrastructure, including servers, storage, and maintenance.

• Azure Data Factory

ADF operates on a pay-as-you-go model, which makes it more predictable and easier to budget for. An organization already using Azure services can easily integrate the costs into Azure’s billing systems.

Security and Compliance

• Airflow

While Airflow has several high-security features, many are custom setups or require third-party plugins. This poses a challenge in setups that have very strict data regulations.

• Azure Data Factory

ADF benefits from Microsoft Azure’s built-in security features and compliance certifications. These include features like data encryption, dedicated HSM, network security, and compliance with global standards like GDPR and HIPAA.

Community and Support

• Airflow

As an open-source project, Airflow has a large and active community. Extensive resources, including documentation, forums, and third-party tutorials, are available. However, official support is limited to what the community provides.

• Azure Data Factory

Microsoft supports ADF, and its support options range from detailed documentation and tutorials to access to Microsoft’s customer service. Therefore, it becomes more reliable for organizations that need it with ensured support.

Pros and Cons

Apache Airflow Pros

1. Highly customizable and flexible
2. Strong support for complex, custom workflows
3. Extensive open-source community

Apache Airflow Cons

1. Steep learning curve.
2. Less suitable for real-time processing.
3. Requires infrastructure management
4. Security and compliance require extra setup

ADF Pros

1. Intuitive, with a graphical interface
2. Fully managed service that automatically scales
3. Easily integrates with Azure Services
4. Security and compliance out of the box

ADF Cons

1. Less flexible for custom workflows
2. Large-scale operations increase overall costs
3. Limited to cloud-based environments
4. The granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague

Which Tool to Choose?

Choose Airflow when:

1. Orchestrating jobs in batch ETL.
2. It automates the organization, execution, and monitoring of data flow.
3. You have complex, custom workflows.
4. Design and implement ETL pipelines to extract batch data from several sources, run Spark jobs, or other data transformation activities.
5. You prefer an open-source solution and have sufficient Python expertise. 

Choose ADF when:

1. The sources and destinations that you use are also within the Azure ecosystem.
2. You must integrate data from on-premises systems across Azure and other cloud platforms.
3. You require advanced analytics capabilities with data processing using Azure Databricks or Azure Synapse Analytics.
4. You want to incorporate machine learning or AI into your ETL processes using Azure Machine Learning.

Why Move Beyond Airflow and Data Factory?

Although Apache Airflow and Azure Data Factory (ADF) are robust solutions, they each have limitations. Apache Airflow is known for its powerful orchestration capabilities but can be complex to set up and maintain, particularly for smaller teams or less technically inclined users. Although very seamless within the Azure ecosystem, ADF will bring a lot of cost and overhead for an organization that needs to be deeply invested in Azure.

A solution to these limitations- Hevo

To overcome these limitations, try Hevo. Hevo is a simple, reliable, no-code platform that fulfills your data migration needs in just a few clicks. Hevo provides: 

1. Competitive and Transparent Pricing: Hevo offers clear, flat pricing tiers that simplify cost management, as well as ADF’s opaque and potentially costly model, especially for smaller businesses.
2. Inclusive Customer Support: While Airflow and Azure Data Factory do not provide adequate customer support, Hevo provides strong, accessible support across all tiers, ensuring that all businesses receive the needed assistance.
3. Flexibility and Customization: Hevo provides greater flexibility and ease of use than Airflow, which can be complex due to the requirement of Python expertise.
4. Ease of Use and Integration: Hevo’s user-friendly interface and seamless integration with 150+ data sources offer a smoother experience than Airflow’s steeper learning curve and Informatica’s integration limitations.

Looking for more comparisons? Check out our blog Matillion vs Airflow: Which One to Choose in 2025? to dive deeper into Airflow’s capabilities!

Conclusion

To conclude, Apache Airflow and Azure Data Factory work seamlessly for data orchestration and integration, and they tend to suit different needs and use cases. So, consider your business needs, extend them by ease of use, scalability, and expense, and choose the best platform to support long-term goals.

Sign up for a 14-day free trial to explore Hevo’s seamless data migration experience.

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