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

KEY TAKEAWAY

• Airflow is ideal for teams that need custom and code-first workflow orchestration.
• Azure Data Factory is best for Azure-centric organizations seeking managed low-code ETL.
• Both tools introduce trade-offs in cost, flexibility, and operational overhead at scale.
• Platforms like Hevo combine ease of use, scalability, and enterprise support to reduce complexity.

Managing and orchestrating data workflows efficiently has become a core challenge for modern data teams. As data volumes grow, pipelines must be reliable, scalable, and easy to maintain. This is where Airflow vs Azure Data Factory becomes a critical comparison for teams evaluating orchestration and integration platforms.

Airflow is widely valued for its code-first flexibility and powerful workflow orchestration, making it a favorite among engineering-led teams. Azure Data Factory, on the other hand, offers a fully managed, low-code experience that integrates seamlessly with the Azure ecosystem.

Both are leading tools, but they solve different problems.

By the end of this guide, you’ll have clarity on which tool best fits your use case, team skillset, and long-term data strategy.

Airflow vs Azure Data Factory vs Hevo: Detailed Comparison Table

	Try Hevo for Free
Primary Use Cases	ETL/ELT for analytics, near real-time replication, SaaS to warehouse	Workflow orchestration, batch pipelines, MLOps scheduling	Cloud-native ETL/ELT, Azure-centric data integration
Connectors	150+ managed connectors for databases, SaaS, and streaming sources	Limited native connectors; relies on operators, plugins, and custom code	90+ built-in connectors, strongest within the Azure ecosystem
Performance & Scalability	Scalable with predictable performance	High scalability with proper infrastructure	Elastic scaling with Azure infrastructure
Core Features & Abilities	No-code pipelines, Python transforms, schema management, and fault tolerance	Python-based DAGs, custom dependencies, flexible scheduling	Low-code GUI pipelines, SSIS migration, Databricks orchestration
Security & Compliance	Built-in encryption, access controls, and compliance-ready	Security depends on custom configuration and plugins	Enterprise-grade Azure security and compliance standards
Cost Model	Predictable subscription pricing, transparent at scale	Open-source with hidden infrastructure and maintenance costs	Pay-as-you-go pricing can grow rapidly with volume
Implementation Complexity	Quick setup with minimal engineering involvement	Steep learning curve; requires strong engineering effort	Moderate; easy UI but Azure expertise required
Vendor / Community Support	Dedicated vendor support with SLAs	Strong open-source community	Microsoft enterprise support

What is Apache Airflow?

G2 Rating: 4.4(120)

Apache Airflow is an open-source workflow orchestration platform to design, schedule, and monitor batch-oriented data pipelines. Workflows are defined in Python, giving teams precise control over execution logic, dependencies, and failure handling.

Instead of relying on visual builders, Airflow treats pipelines as code. This makes workflows version-controlled, testable, and easier to maintain as systems scale. Airflow can run as a single local process or as a distributed deployment across multiple machines. It is suitable for both small teams and large data platforms.

Key features of Apache Airflow

• Human-in-the-Loop (HITL) workflows: Airflow 3.1.0 introduces HITL tasks that pause pipelines and surface web forms in the UI for manual review and approval before workflows continue.
• Extensible UI with React & FastAPI: Enhance the Airflow interface using embedded React apps, FastAPI sub-apps, and custom middleware for tools like lineage or observability.
• 80+ provider packages: Community-built providers extend Airflow with integrations, secret management, notifications, and logging auto-discovered on install.
• Backfill & selective re-runs: Trigger historical DAG runs or re-execute only failed tasks from the UI to ensure data completeness without reprocessing everything.
• Global-ready platform: Supports 17-language internationalization, which makes it accessible for distributed and global teams.

Pros

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

Cons

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

Common use cases of Apache 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 the Path API that simplifies 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.

Customer testimonial

For me, the standout feature is definitely the Web UI. As a data engineer, I often find myself troubleshooting, and the Grid view in Airflow makes it remarkably simple to identify exactly where a pipeline has failed. I can quickly access the logs for any specific task and determine what went wrong within seconds. This level of transparency is something that traditional cron jobs or basic scripts simply don\'t offer. Having a central dashboard for all your workflows truly provides peace of mind.

Aindrila R

Assistant System Engineer, Computer Software

View Review

What is Azure Data Factory?

G2 Rating: 4.6(90)

Azure Data Factory (ADF) is Microsoft’s fully managed and cloud-native data integration service to build, schedule, and orchestrate data pipelines at scale. It enables teams to move and transform data across on-premises systems, cloud platforms, and SaaS applications without managing underlying infrastructure.

ADF abstracts compute, scaling, and availability, so that data teams can focus on pipeline logic rather than operations. Its tight integration with Azure services makes it a natural choice for organizations running analytics and data platforms on Microsoft Azure.

Key features of Azure Data Factory

• Tumbling window triggers: Enable sequential, non-overlapping pipeline runs ideal for time-series and gap-free data processing.
• SSIS lift-and-shift migration: Run and monitor on-prem SSIS packages in the cloud using Integration Runtime, without rewriting existing ETL and ELT logic.
• Interactive pipeline debugging: Test and debug pipelines directly in the browser before production deployment.
• Native Databricks orchestration: Execute Azure Databricks Notebooks as pipeline steps with parameterized inputs for dynamic workflows.
• Multi-language SDK support: Manage pipelines programmatically using Python, .NET, REST, or PowerShell SDKs.

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

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 vs Airflow provides error messages that are too generic or vague

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 for streamlined version control and collaborative development.

Customer testimonial

What I like best about Azure Data Factory is its robust and versatile data integration capabilities. It offers a wide range of connectors and tools to efficiently manage and transform data from various sources. Its user-friendly interface, combined with the flexibility to handle complex workflows, makes it an excellent choice for orchestrating data pipelines. The seamless integration with other Azure services also enhances its functionality, making it a powerful tool for data engineering tasks.

Sowjanya G.

Digital Education Student Ambassador

View Review

Airflow vs Azure Data Factory: Head-to-Head Comparison

Although both platforms support modern data pipelines, they solve different problems in different ways. The sections below highlight where each tool excels and where trade-offs may appear to help you match technical capabilities with practical business requirements.

Ease of use

The learning curve and workflow design experience directly affect how fast teams can build pipelines and respond to changing data needs.

Integration and compatibility

Seamless integration reduces development effort and ensures data moves reliably across systems and services.

Scalability and performance

As data volumes and workloads increase, the platform you choose must scale without introducing operational bottlenecks.

Cost considerations

Total cost depends not only on pricing models but also on infrastructure, maintenance, and engineering effort.

Security and compliance

Strong governance capabilities are critical when handling sensitive data and regulated workloads.

Community and support

Reliable support channels help teams resolve issues faster and maintain pipeline stability.

Which Tool Should You Choose?

Choosing between Apache Airflow and Azure Data Factory depends on your infrastructure and long-term data strategy.

Choose Apache Airflow if:

• You primarily orchestrate batch-based ETL/ELT workflows.
• You need to automate job scheduling, execution, dependencies, and monitoring.
• Your workflows are complex, highly customized, or non-standard.
• You design pipelines that extract batch data from multiple sources and run Spark jobs or custom transformations.
• You prefer an open-source solution and have strong Python and infrastructure expertise.

Choose Azure Data Factory (ADF) if:

• Most of your data sources and destinations already live within the Azure ecosystem.
• You need to integrate on-premises systems with Azure and other cloud platforms.
• Your pipelines rely on Azure Databricks or Azure Synapse Analytics for large-scale processing.
• You plan to embed machine learning or AI workflows using Azure Machine Learning.
• You want a low-code, fully managed service with minimal operational overhead.

Why Move Beyond Airflow and Azure Data Factory?

When evaluating Azure Data Factory vs Airflow, you’ll notice that both are powerful tools, but they introduce trade-offs that can slow teams down as data needs scale.

• Apache Airflow offers deep orchestration control but demands significant Python expertise, infrastructure management, and ongoing maintenance. This makes it challenging for lean or fast-moving teams.
  
• Choosing between Azure Data Factory vs Airflow often comes down to trade-offs: Data Factory works seamlessly within the Azure ecosystem but can be costly and platform-locked, while Airflow provides greater flexibility across cloud and on-prem environments.

For many organizations, these limitations create operational friction rather than efficiency.

The Solution: A Simpler & More Balanced Approach with Hevo

Hevo addresses these challenges by combining ease of use, reliability, and flexibility in a no-code data integration platform.

With Hevo, teams get the following advantages:

• Simple to use: Hevo blends ease of use, reliability, and flexibility in a fully managed, no-code ELT platform. Teams can get started in minutes without scripting, infrastructure setup, or ongoing maintenance.
• Transparent and predictable pricing: Hevo offers competitive and transparent pricing with predictable tiers. There are no hidden or usage-based cost spikes often seen with Azure Data Factory.
• Reliable by design: Unlike Airflow and ADF, where support is limited or tier-restricted, Hevo provides inclusive customer support across all plans. This ensures help is always accessible when pipelines matter most.
• Flexible without operational overhead: Hevo delivers high flexibility without heavy engineering effort. It removes Airflow’s dependency on deep Python expertise and infrastructure management while still supporting complex data movement needs.
• Fast & scalable integrations: Fast and seamless integration with 150+ data sources enables quicker setup, automated scaling, and smoother pipeline management that keeps data flowing reliably as volumes grow.

Looking for more comparisons? Explore:

• Matillion vs Airflow: Which One to Choose in 2025?
• Azure ETL tools for data engineers
• Top data ingestion tools in Azure

Make the Right Choice for Scalable Data Orchestration

To sum up, Apache Airflow and Azure Data Factory work great for data orchestration and integration, but they are designed to address different needs and use cases. When choosing between them, evaluate your business requirements in terms of ease of use, scalability, and cost to determine which platform best supports your long-term goals.

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

FAQs

Arjun Narayan Product Manager

Arjun Narayanan is a Product Manager at Hevo Data. With 6 years of experience, he leverages his strategic vision and technical expertise to drive innovation. Arjun excels in product development, competitive analysis, and delivering scalable data solutions, making him a key asset in the data industry.