10 Best Airflow Alternatives for 2025

Apache Airflow is a workflow orchestration platform for orchestrating distributed applications. It leverages DAGs (Directed Acyclic Graphs) to schedule jobs across several servers or nodes. Airflow is also used to manage Data Pipelines. As the data environment evolves, Airflow frequently encounters challenges in the areas of testing, non-scheduled processes, parameterization, data transfer, and storage abstraction. 

To help you with the above challenges, this article lists down the best Airflow Alternatives along with their key features. You can try out any or all and select the best according to your business requirements. Along with the details on Apache Airflow Alternatives, let’s discuss what is Airflow, its key features, and some of its shortcomings that led you to this page.

Why Look for an Apache Airflow Alternative?

• Complexity in Setup and Maintenance: Apache Airflow can be difficult to install, configure, and manage, requiring significant resources and expertise.
• Steep Learning Curve: The platform’s reliance on Python for defining workflows (DAGs) and its intricate configuration options can be challenging for non-developers or those new to the tool.
• Limited Real-Time Processing: Airflow’s focus on batch processing makes it less suitable for dynamic or real-time data workflows, prompting users to seek alternatives better suited for these needs.

Top 10 Apache Airflow Alternatives

Below is a comprehensive list of top Airflow competitors that can be used to manage orchestration tasks while providing solutions to overcome the above-listed problems.

1) Hevo Data

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (60+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready. Like Airflow, Hevo also has the capability to automate workflow through its intuitive UI.

Key features of Hevo are,

• Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
• Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.

Check Hevo’s in-depth documentation to learn more.

2) Luigi

Among a popular choice for an Apache Airflow alternative is Luigi. It is a Python package that handles long-running batch processing. This means that it manages the automatic execution of data processing processes on several objects in a batch. A data processing job may be defined as a series of dependent tasks in Luigi.

Luigi figures out what tasks it needs to run in order to finish a task. It provides a framework for creating and managing data processing pipelines in general. It was created by Spotify to help them manage groups of jobs that require data to be fetched and processed from a range of sources.

Key Features of Luigi that make it a pretty great Airflow alternative:

• Modular: Luigi breaks down your monolithic web applications into a series of UI modules that mirror their underlying backend modularity.
• Extensible: Luigi allows you to securely combine UI modules from external systems and construct applications that can be expanded with new functionality from your client or a third-party vendor.
• Scalable: Luigi separates and distributes the creation of end-to-end features among an indefinite number of teams, allowing them to build, launch, manage, and run their solutions independently and quickly.
• Technology Agnostic: Technology evolves rapidly. You never know if the technology you select today will be obsolete in a year’s time. Luigi is technology agnostic, allowing you to easily respond to new trends and avoid technological lock-in.

3) Apache NiFi

Apache NiFi is a free and open-source application that automates data transfer across systems. The application comes with a web-based user interface to manage scalable directed graphs of data routing, transformation, and system mediation logic. It is a sophisticated and reliable data processing and distribution system. To edit data at runtime, it provides a highly flexible and adaptable data flow method.

Key Features of Apache NiFi:

• Highly Configurable: Apache NiFi has a lot of configuration options. This enables customers to achieve assured delivery, high throughput, low latency, dynamic prioritization, back pressure, and runtime flow modification.
• Web-Based User Interface: The web-based user interface for Apache NiFi is simple to use. Design, control, and feedback monitoring can all be done through the web UI, with no additional resources required. This provides customers with a simple web-based interface and a seamless design, control, feedback, and monitoring experience.
• Built-in Monitoring: A data provenance module in Apache NiFi allows you to track and monitor data from start to finish. Developers can design their own custom processors and reporting activities to meet their own requirements.
• Support for Secure Protocols: Secure protocols such as SSL, HTTPS, SSH, and a range of additional encryptions are also supported by Apache NiFi. In a range of complicated corporate situations, this leads to a highly secure architecture.
• Good User & Role Management: User role management is supported by Apache NiFi, which may also be set to use LDAP for authorization. Administrators can define thresholds for different users to enable them to read and edit regulations, access the controller, and get site-to-site data, or to prevent them from accessing any functions at all.

4) AWS Step Functions

AWS Step Function from Amazon Web Services is a completely managed, serverless, and low-code visual workflow solution. AWS Step Functions can be used to prepare data for Machine Learning, create serverless applications, automate ETL workflows, and orchestrate microservices.

AWS Step Functions enable the incorporation of AWS services such as Lambda, Fargate, SNS, SQS, SageMaker, and EMR into business processes, Data Pipelines, and applications. Users and enterprises can choose between 2 types of workflows: Standard (for long-running workloads) and Express (for high-volume event processing workloads), depending on their use case.

Key Use Cases of AWS Step Functions:

• Automate Extract, Transform, and Load (ETL) Process: Rather than manually orchestrating or maintaining a separate application, AWS Step Functions ensure that long-running, numerous ETL operations execute in sequence and conclude properly.
• Prepare Data for Machine Learning (ML): Source data must be gathered, processed, and normalized before ML modeling systems such as Amazon SageMaker can train on it. Step Functions make it easy to sequence the stages in your ML pipeline automation.
• Orchestrate Microservices: To create responsive serverless applications and microservices, you can leverage AWS Step Functions to integrate numerous AWS Lambda functions. Data and services running on Amazon EC2 instances, containers, or on-premises servers can also be orchestrated.

5) Prefect

Prefect is transforming the way Data Engineers and Data Scientists manage their workflows and Data Pipelines. Prefect decreases negative engineering by building a rich DAG structure with an emphasis on enabling positive engineering by offering an easy-to-deploy orchestration layer for the current data stack. As a result, data specialists can essentially quadruple their output.

Prefect blends the ease of the Cloud with the security of on-premises to satisfy the demands of businesses that need to install, monitor, and manage processes fast. It has helped businesses of all sizes realize the immediate financial benefits of being able to swiftly deploy, scale, and manage their processes. Unlike Apache Airflow’s heavily limited and verbose tasks, Prefect makes business processes simple via Python functions.

Key Features of Prefect:

• Prefect Python Library: Prefect Python is a Python package that makes it easier to design, test, operate, and construct complicated data applications. It has a user-friendly API that doesn’t require any configuration files or boilerplate. It allows for process orchestration and monitoring using best industry practices.
• Real-Time User Interface: Prefect comes with a consistent, real-time interface that allows you to keep track of state updates and logs, start new runs, and collect critical data as needed. Its dashboard provides access to recent run summaries, scheduled run descriptions, error log links, and activity timelines.
• Comprehensive Task Library: Prefect has a large and growing task library with predefined tasks including running shell scripts, sending tweets, and managing Kubernetes jobs.
• Rich State Objects: For communicating information about tasks and flows, Prefect provides rich state objects. By analyzing the current state and the history of task states, users can implement custom logic to respond to states and learn about tasks/flows.
• Community Support: Prefect fits precisely with best practices, allows online services, and effectively supports Data Science boot camps and Fortune-100 organizations, thanks to the contributions of hundreds of Engineers and Data Scientists.

6) Dagster

Dagster is a Machine Learning, Analytics, and ETL Data Orchestrator. Since it handles the basic function of scheduling, effectively ordering, and monitoring computations, Dagster can be used as an alternative or replacement for Airflow (and other classic workflow engines).

However, it goes beyond the usual definition of an orchestrator by reinventing the entire end-to-end process of developing and deploying data applications. Dagster is designed to meet the needs of each stage of the life cycle, delivering:

• The process of creating and testing data applications. Practitioners are more productive, and errors are detected sooner, leading to happy practitioners and higher-quality systems.
• An orchestration environment that evolves with you, from “single-player mode” on your laptop to a multi-tenant business platform.
• Consumer-grade operations, monitoring, and observability solution that allows a wide spectrum of users to self-serve.

Key Features of Dagster:

• Flexibility: When it comes to allocating computing resources, users have a lot of options. Dagster allows you to manage the execution from a variety of contexts while keeping your business logic the same.
• Horizontal Scalability: Each run-specific computing operation runs independently. It scales horizontally.
• Fast Navigation: The organized event log enables quick access to essential data, such as error messages. Users can quickly locate them and view well-formatted stack traces with only a few keystrokes.
• Lightweight Python Execution APIs: Dagster pipelines can run entirely in memory, without the need for a database or a scheduler.
• Independent, Atomic Deployment: Dagster comes with atomic deployment. Users can update code in the repository without restarting the system. Atomic deployment is more reliable than reloading code regularly.

Read Moving Past Airflow: Why Dagster is the next-generation data Orchestrator to get a detailed analysis of Airflow vs Dagster.

7) Kedro

Kedro is an open-source Python framework for writing Data Science code that is repeatable, manageable, and modular. Modularity, separation of concerns, and versioning are among the ideas borrowed from software engineering best practices and applied to Machine Learning algorithms.

Key Features of Kedro:

• Execution Timeline: A Kedro pipeline’s execution timeline can be viewed as a series of operations carried out by several Kedro library components including DataSets, DataCatalog, Pipeline, and Node. You can add extra behavior at different stages in the lifespan of these components.
• Integrate Kedro with DataSets: DataSets can be used to connect to a variety of data sources. You can generate a custom dataset if the data source you want to use isn’t supported by Kedro out of the box.
• Add CLI Commands: Plugins can be used to insert extra CLI commands that will be reused across projects. Kedro plugins allow you to extend Kedro’s functionality and inject new commands into the CLI. Plugins are created as stand-alone Python packages that are explicit to any Kedro project.

8) Apache Oozie

One of the workflow scheduler services/applications operating on the Hadoop cluster is Apache Oozie. It is used to handle Hadoop tasks such as Hive, Sqoop, SQL, MapReduce, and HDFS operations such as distcp. It is a system that manages the workflow of jobs that are reliant on each other. Users can design Directed Acyclic Graphs of processes here, which can be performed in Hadoop in parallel or sequentially.

Apache Oozie is one of the workflow orchestration tools that are quite adaptable. Jobs can be simply started, stopped, suspended, and restarted. Rerunning failed processes is a breeze with Oozie. It’s even possible to bypass a failed node entirely.

Key Features of Apache Oozie:

• It includes a client API and a command-line interface that can be used to start, control, and monitor jobs from Java applications.
• Its Web Service APIs allow users to manage tasks from anywhere. 
• It offers the ability to run jobs that are scheduled to run regularly.
• It provides the ability to send email reminders when jobs are completed.

Well, this list could be endless. However, this article lists the best alternatives to Airflow in the market. Hope these Apache Airflow Alternatives help solve your business use cases effectively and efficiently.

9) Astronomer

Astronomer is a modern platform that runs Apache Airflow for you and builds pipelines to power the analytical workloads. It offers a managed service by simplifying deployment and maintenance. It helps you  to build, run, and observe pipelines as code. 

Astronomer acts as a layer for seamless integration with Apache Airflow. Without directly managing the infrastructure of Astronomer you can leverage the capabilities of Apache airflow, ensuring best designs and execution of data pipelines. 

Key features of Astronomer:

• Cloud Integration: Astronomer integrate with various cloud services like AWS,Google cloud, and Azure at an ease. It ensures compatibility with popular services and reduces complex deployments. 
• Python Building Blocks: Astronomer allows you to leverage the power of python scripting for building pipelines. 
• SQL Building Blocks: Astronomer supports SQL Building Blocks for Querying Data Sources, Data transformations, and Source Destination Mappings.

• Astronomer overcomes the challenges associated with local testing and debugging in Apache Airflow by introducing its proprietary Continuous Integration/Continuous Deployment (CI/CD) tool.

10) Azure Data Factory

Data Factory is a workflow management and cloud-based data integration service provided by Microsoft Azure. Users can create and manage data pipelines using the user interface, making it accessible to diverse users. It is designed for ETL and ELT scenarios, allowing users to prepare and transform data as part of the data pipeline.

But, If you need a platform-neutral, open-source solution with a strong focus on technical expertise, Apache Airflow is a better choice.

Limitations of Apache Airflow

After reading the key features of Airflow in this article above, you might think of it as the perfect solution. However, like a coin has 2 sides, Airflow also comes with certain limitations and disadvantages. Some of the Apache Airflow platform’s shortcomings are listed below:

• High Learning Curve: Since Apache Airflow has a steep learning curve, it can be difficult for users, particularly novices, to acclimate to the environment and complete tasks like writing test cases for Data Pipelines that handle raw data.
• Renaming Issues: Every time you modify your schedule intervals, Apache Airflow asks you to rename your DAGs to guarantee that your prior task instances are aligned with the new time period.
• Removes Metadata: Since Apache Airflow’s Data Pipelines lack a version control system if you delete a job from your DAG code and then redeploy it, all the metadata associated with the transaction is automatically erased.
• Outdated Scheduler: Apache Airflow scheduler has an outdated design. Users can get confused in the process of task triggering as all tasks are executed through a date scheduler. In addition, the scheduler hinders triggering of the same task simultaneously. To simulate job repetition, users need to create two similar tasks. The tough part is, tasks are scheduled using the execution_date parameter, which are interpreted as the end of an interval capped by the DAG’s start time but not as the start time for a DAG.

Hence, you can overcome these shortcomings by using the above-listed Airflow Alternatives.

Factors To Consider For Choosing Apache Airflow Alternatives

Consideration	Description	Why It Matters
Scalability	Ability to handle increasing data and workflows, supporting distributed execution.	Ensures the tool can scale with growing workloads and large datasets.
Ease of Use	User-friendly interfaces (UI/CLI), simple configuration, and debugging tools.	Facilitates smooth workflow creation and management, reducing the learning curve.
Integration with Existing Tools	Compatibility with cloud platforms, databases, data warehouses, and third-party services.	Ensures seamless data flow and orchestration across your tech stack.
Flexibility & Extensibility	Support for custom operators, plugins, and complex workflows (e.g., branching).	Enables customization and adaption to specific use cases.
Scheduling & Monitoring	Built-in scheduling, task triggering, and alerting on failures or delays.	Helps automate task execution and ensures smooth operations with effective monitoring.
Community & Support	Active community, documentation, and availability of commercial support.	A strong community and support network ensure quicker problem-solving and tool enhancements.
Cost	Pricing model and operational costs, especially for managed services.	Ensures the solution fits within budget, considering both infrastructure and licensing costs.
Data Provenance & Lineage	Tracking data flow and transformations.	Helps with data governance, transparency, and auditability.
Latency & Real-time Processing	Support for low-latency, event-driven tasks for real-time processing.	Ensures the system can meet performance requirements for real-time data processing.
Reliability & Fault Tolerance	Task retries, fault tolerance, and automatic failover mechanisms.	Minimizes downtime and ensures reliable task execution under failure conditions.

Conclusion

In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped you explore the best Apache Airflow Alternatives available in the market. So, you can try hands-on on these Airflow Alternatives and select the best according to your use case.

However, extracting complex data from a diverse set of data sources like CRMs, Project management Tools, Streaming Services, and Marketing Platforms can be quite challenging.

This is where a simpler alternative like Hevo can save your day! Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 150+ Data Connectors including 60+ Free Sources, into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code. Connect with us today to improve your data management experience and achieve more with your data.

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