7 Best Airflow Alternatives for 2023

• January 10th, 2023

Apache Airflow is a workflow orchestration platform for orchestrating distributed applications. It leverages DAGs(Directed Acyclic Graph) to schedule jobs across several servers or nodes. Apache Airflow has a user interface that makes it simple to see how data flows through the pipeline. You can also examine logs and track the progress of each task.

Thousands of firms use Airflow to manage their Data Pipelines, and you’d be challenged to find a prominent corporation that doesn’t employ it in some way. With that stated, 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. Before you jump to the Airflow Alternatives, let’s discuss what is Airflow, its key features, and some of its shortcomings that led you to this page.

Table of Contents

What is Apache Airflow?

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Apache Airflow is a workflow authoring, scheduling, and monitoring open-source tool. It’s one of Data Engineers’ most dependable technologies for orchestrating operations or Pipelines. Your Data Pipelines’ dependencies, progress, logs, code, trigger tasks, and success status can all be viewed instantly. Airflow has become one of the most powerful open source Data Pipeline solutions available in the market.

Airflow was built to be a highly adaptable task scheduler. Its usefulness, however, does not end there. It’s also used to train Machine Learning models, provide notifications, track systems, and power numerous API operations. Airflow also has a backfilling feature that enables users to simply reprocess prior data. This functionality may also be used to recompute any dataset after making changes to the code.

Airflow Alternatives: Airflow DAG
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Users may design workflows as DAGs (Directed Acyclic Graphs) of tasks using Airflow. Airflow’s powerful User Interface makes visualizing pipelines in production, tracking progress, and resolving issues a breeze. It integrates with many data sources and may notify users through email or Slack when a job is finished or fails. It is perfect for orchestrating complex Business Logic since it is distributed, scalable, and adaptive.

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Key Features of Apache Airflow

Apache Airflow is used by many firms, including Slack, Robinhood, Freetrade, 9GAG, Square, Walmart, and others. Let’s take a glance at the amazing features Airflow offers that makes it stand out among other solutions:

  • Easy to Use: Anyone who is familiar with the Python programming language can easily set up an Airflow Data Pipeline. It allows users to create Machine Learning models, manage infrastructure, and transmit data, with no limitations on pipeline scope. Furthermore, it allows users to resume work from where they left off without having to restart the entire workflow.
  • Robust Pipelines: Airflow pipelines are basic and explicit. It incorporates the sophisticated Jinja template engine into its core, allowing you to parameterize your scripts. In addition, users may execute pipelines at regular intervals thanks to the sophisticated scheduling semantics.
  • Scalable: Airflow is a modular approach that uses a message queue to orchestrate an arbitrary number of workers. It is a general-purpose orchestration framework with an easy-to-understand set of functionality.
  • High Extensibility with Robust Integrations: Airflow has a large number of operators accessible who are prepared to do the work on the Google Cloud Platform, Amazon Web Services, and a range of other third-party services. As a result, it’s simple to integrate into existing infrastructure and scale up to next-generation technology.
  • Pure Python: Airflow enables users to build Data Pipelines using standard Python capabilities such as data time formats for scheduling and loops for dynamically generating jobs. This allows users to create Data Pipelines with as much flexibility as possible.

Want to explore other key features and benefits of Apache Airflow? Refer to the Airflow Official Page

To overcome some of the Airflow limitations discussed at the end of this article, new robust solutions i.e. Airflow Alternatives were introduced in the market. Read along to discover the 7 popular Airflow Alternatives being deployed in the industry today.

Top 7 Apache Airflow Alternatives

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

1) Airflow Alternatives: Luigi

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Luigi 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.

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Key Features of Luigi:

  • 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.

Explore more about Luigi here.

2) Airflow Alternatives: Apache NiFi

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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.

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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.

Explore more about Apache NiFi here.

All of the capabilities, none of the firefighting

Using manual scripts and custom code to move data into the warehouse is cumbersome. Frequent breakages, pipeline errors and lack of data flow monitoring makes scaling such a system a nightmare. Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work.

  • Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs 
  • Stay in Total Control: When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
  • Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
  • 24×7 Customer Support: With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-featured free trial.
  • Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in data flow. 

3) Airflow Alternatives: AWS Step Functions

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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.

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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.

Explore more about AWS Step Functions here.

4) Airflow Alternatives: Prefect

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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.

Airflow Alternatives: Prefect Example
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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 practises, allows online services, and effectively supports Data Science bootcamps and Fortune-100 organizations, thanks to the contributions of hundreds of Engineers and Data Scientists.

Explore more about Prefect here.

5) Airflow Alternatives: Dagster

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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.
Airflow Alternatives - Dagster Workflow
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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 comparative analysis of Airflow and Dagster.

6) Airflow Alternatives: Kedro

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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.

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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.

Explore more about Kedro here.

7) Airflow Alternatives: Apache Oozie

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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 also 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.

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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 down the best Airflow Alternatives in the market. Hope these Apache Airflow Alternatives help solve your business use cases effectively and efficiently.

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.

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


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, 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 40+ 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.


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Share your experience with Airflow Alternatives in the comments section below!

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