Best Airflow Alternatives for Data Orchestration in 2024

Introduction

Apache Airflow has established itself as a standard for orchestrating workflows, especially for large-scale ETL
(Extract, Transform, Load) jobs, machine learning pipelines, and complex data engineering projects. However,
despite its popularity, Airflow isn’t always the best fit for every use case due to challenges like complexity
in setup, limited support for modern cloud-native applications, and its dependency on Python-based DAG
definitions. Fortunately, a range of alternatives has emerged that address these challenges, offering more
flexibility, scalability, or ease of use. Below, we’ll take a deep dive into ten top alternatives to Apache
Airflow in 2024, exploring their key features, strengths, and ideal use cases.

Overview of Apache Airflow and Its Limitations

Airflow has limitations that may not suit every organization, particularly when it comes to complexity in setup
and its dependency on Python-based DAGs. Many modern cloud-native applications require more user-friendly
solutions.

Why Consider Alternatives to Airflow?

While Airflow is a robust tool, alternatives provide ease of use, flexibility, and scalability that can better
meet the needs of modern data pipelines, cloud-native applications, and specific workflows such as machine
learning pipelines.

Top 10 Airflow Alternatives in 2024

1. Prefect

Prefect is one of the leading alternatives to Airflow, offering a more intuitive and user-friendly approach to
orchestrating data workflows. Prefect reduces “negative engineering”—the time and effort spent fixing broken
workflows. It does so by providing a rich, easy-to-use orchestration layer that integrates seamlessly with
existing data stacks. One of its main advantages over Airflow is its emphasis on reducing code verbosity, making
it easier to write and manage workflows.

Key Features:

  • Prefect Python Library: Prefect comes with a lightweight Python package that
    simplifies the design, testing, and execution of workflows. It eliminates the need for configuration
    files and minimizes boilerplate code.
  • Real-Time Dashboard: Prefect’s dashboard allows users to monitor workflows in
    real-time, providing detailed logs, error messages, and state updates. This is especially helpful
    for long-running tasks that require continuous monitoring.
  • Rich State Management: Prefect offers robust state management, enabling users to
    track and respond to different states of workflow tasks. This allows for greater flexibility in
    handling failures or reruns.
  • Task Library: Prefect comes with a comprehensive library of tasks that can be
    easily customized, allowing users to manage a variety of actions, from running shell scripts to
    managing Kubernetes jobs.

Use Case:

Prefect is an excellent choice for data engineers and data scientists who want a simpler, more efficient way
to manage workflows. It’s ideal for teams that need to orchestrate complex pipelines but want to avoid the
high overhead of managing an Airflow installation.

2. Hevo Data

Hevo Data is a leading real-time ELT No-code Data Pipeline platform designed to automate data pipelines cost-effectively while offering the flexibility to meet your specific needs. With integrations across 150+ Data Sources (including 40+ free sources), Hevo not only facilitates the export of data from sources and the loading of data to destinations, but also transforms and enriches the data, ensuring it is analysis-ready. As an Airflow alternative, Hevo provides an intuitive UI to automate workflows without the need for complex coding.

Key Features:

  • Data Transformation: Hevo provides a user-friendly interface to clean, modify, and
    enrich your data, ensuring it meets the desired format.
  • Schema Management: Hevo automatically detects and manages the schema of incoming
    data, mapping it seamlessly to the schema of the destination.
  • Incremental Data Load: Hevo enables the transfer of only the modified data in real
    time, optimizing bandwidth usage at both source and destination ends.

Use Case:

Hevo Data is a great solution for teams seeking a no-code platform for real-time ELT and pipeline automation
without the need for coding expertise.

3. Kedro

Kedro, developed by QuantumBlack, is an open-source Python framework aimed at making data science workflows
repeatable and maintainable. While not a direct competitor to Airflow, it excels at integrating best practices
from software engineering into data science pipelines, making it a great alternative for machine learning and
data science projects.

Key Features:

  • Modular Pipelines: Kedro encourages modularity and separation of concerns, allowing
    teams to break down complex workflows into manageable components.
  • Version Control: It supports version control, ensuring that changes to the pipeline
    can be tracked and audited, which is crucial for reproducibility.
  • Data Abstraction: Kedro provides tools for abstracting data sources and sinks,
    making it easier to work with a variety of data storage formats.
  • CLI Tools: Kedro includes command-line tools that simplify running, testing, and
    managing pipelines, allowing for greater automation and faster development cycles.

Use Case:

Kedro is ideal for data science teams working on machine learning models who want to bring more structure
and software engineering practices into their workflows. It’s not as robust as some other alternatives for
ETL, but it excels in repeatable, modular data science tasks.

4. AWS Step Functions

AWS Step Functions is a serverless orchestration service that enables you to coordinate multiple AWS services
into a single workflow. If your data stack is built around AWS services, Step Functions provide a highly
integrated solution for orchestrating everything from ETL jobs to complex serverless applications.

Key Features:

  • Serverless Architecture: Since Step Functions is a fully managed service, there’s
    no need to worry about provisioning or managing servers.
  • Native AWS Integration: It integrates seamlessly with other AWS services like
    Lambda, EC2, S3, and DynamoDB, making it ideal for AWS-heavy environments.
  • Visual Workflow Builder: Step Functions provide a visual interface for building
    workflows, making it easier to design, test, and debug complex pipelines.
  • Automated Retries and Error Handling: The service automatically retries failed
    steps and allows for customizable error handling, reducing the need for manual intervention.

Use Case:

AWS Step Functions are ideal for teams already using AWS services that need to orchestrate workflows across
the AWS ecosystem. Its serverless nature makes it a good fit for microservice-based architectures and
event-driven applications.

5. Google Cloud Composer

Google Cloud Composer is a fully managed orchestration service built on Apache Airflow. It provides a way to run
and manage Airflow without the operational overhead of setting up and maintaining the infrastructure. Cloud
Composer integrates natively with Google Cloud services, making it a powerful solution for users already
committed to the Google Cloud ecosystem.

Key Features:

  • Managed Airflow: Cloud Composer allows users to focus on building and managing
    workflows without worrying about maintaining the Airflow infrastructure.
  • Google Cloud Integration: It integrates natively with services like BigQuery,
    Google Cloud Storage, and Google Dataflow, providing a seamless experience for users working within
    the Google Cloud environment.
  • Automatic Scaling: Composer automatically scales the underlying infrastructure to
    meet the needs of your workflows, ensuring that they run smoothly even as data volumes grow
  • Secure and Compliant: As a Google Cloud service, Composer benefits from Google’s
    extensive security features and compliance certifications, making it a good choice for teams with
    strict security and compliance requirements.

Use Case:

Google Cloud Composer is an excellent choice for teams already using Google Cloud services who want the
power of Apache Airflow without the overhead of managing the infrastructure. It’s particularly well-suited
for orchestrating workflows that involve other Google Cloud services.

6. Argo

Argo is a Kubernetes-native workflow orchestrator designed for cloud-native applications. It’s particularly
useful for orchestrating containerized workflows and microservices, making it an excellent choice for teams
already using Kubernetes.

Key Features:

  • Container-Native: Argo is designed to run workflows as Kubernetes pods, making it a
    natural fit for teams using Kubernetes for their applications.
  • Event-Driven Architecture: Argo supports event-driven workflows, enabling users to
    trigger workflows based on events like changes in data or the status of other services.
  • Scalability: Since it runs on Kubernetes, Argo can scale horizontally to handle
    large-scale workflows and data processing tasks.
  • Workflow Versioning: Argo supports versioning of workflows, ensuring that changes
    to pipelines can be tracked and rolled back if necessary.

Use Case:

Argo is perfect for teams building cloud-native, containerized applications on Kubernetes. Its scalability
and event-driven architecture make it ideal for large, distributed workflows.

7. Flyte

Flyte is another Kubernetes-native orchestrator designed specifically for managing large-scale, distributed
systems. It is well-suited for machine learning and big data applications and offers robust version control and
monitoring features.

Key Features:

  • Kubernetes-Native: Like Argo, Flyte runs on Kubernetes, making it highly scalable
    and suited for cloud-native architectures.
  • Workflow Versioning: Flyte offers strong version control, allowing teams to track
    changes to their workflows and ensuring reproducibility across different environments.
  • Resource Management: Flyte allows teams to allocate resources dynamically based on
    the needs of individual tasks, ensuring efficient use of computing power.

Use Case:

Flyte is ideal for teams working on machine learning or big data projects that require robust version
control and scalability. Its Kubernetes-native architecture makes it a strong choice for cloud-native
applications.

8. Kestra

Kestra is a relatively new entrant to the workflow orchestration space but has quickly gained popularity due to
its ability to handle large-scale, event-driven workflows. It is designed to orchestrate real-time data
pipelines and event streams, making it ideal for use cases that require low-latency data processing.

Key Features:

  • Event-Driven Architecture: Kestra is designed to handle real-time data pipelines,
    making it a good fit for teams working with streaming data or low-latency applications.
  • Unified Interface: It provides a unified interface for managing and monitoring
    workflows across different environments, making it easier
  • Distributed Workflow Execution: Supports large-scale distributed pipelines.

Use Case:

Apache Airflow is a popular open-source platform that supports complex, data-driven workflows, but its
limitations push some users to seek alternatives. Many modern tools now offer more user-friendly interfaces,
cloud-native capabilities, and advanced orchestration features. In this expanded guide, we’ll explore ten
leading Airflow alternatives in 2024, offering insights into their unique capabilities and how they can
serve diverse data engineering and data science needs.

9. Metaflow

Metaflow, originally developed by Netflix, is a human-centric workflow tool designed specifically for data
science and machine learning workflows. It focuses on simplifying complex machine learning pipelines and
enhancing collaboration among teams of data scientists.

Key Features:

  • Data Science-Focused: Metaflow excels at simplifying the workflow management
    process for machine learning and data science, offering integrations with popular libraries like
    TensorFlow and PyTorch.
  • Human-Centric Design: The platform is designed to be user-friendly, targeting data
    scientists who may not have deep software engineering experience.
  • Scalable on AWS: Metaflow has deep integration with AWS, enabling it to scale
    workloads efficiently on cloud infrastructure. It supports scaling computations across large
    datasets by leveraging cloud-based instances for training models or running workflows.

Use Case:

Metaflow is an excellent alternative to Airflow for machine learning and data science teams looking for an
orchestration platform that offers built-in version control, scalability, and simplicity. It’s particularly
well-suited for teams needing to manage end-to-end machine learning pipelines with minimal engineering
overhead.

10. Mage

Mage is a relatively new, low-code workflow orchestration platform designed for simplicity and ease of use.
While it’s still evolving, Mage has gained popularity due to its focus on democratizing data workflows, making
it accessible to non-technical users.

Key Features:

  • Low-Code Interface: Mage provides an intuitive, low-code interface that enables
    users with little programming experience to create and orchestrate workflows. This makes it
    particularly attractive for teams that want to onboard non-technical users into data orchestration
    projects.
  • Support for Machine Learning Workflows: Mage supports complex workflows involving
    machine learning, allowing users to easily orchestrate model training and deployment processes
    without extensive programming knowledge.
  • Built-In Integrations: Mage offers out-of-the-box integrations with various cloud
    platforms and data sources, making it easier to connect different parts of the data stack without
    additional configuration.
  • User-Friendly Design: Mage’s UI is designed with simplicity in mind, offering
    drag-and-drop functionality for building workflows and monitoring their performance.

Use Case:

Mage is ideal for teams that want to empower non-technical users to build and manage data workflows. Its
low-code nature makes it accessible for less-experienced users while still supporting complex machine
learning and data processing pipelines.

Conclusion

When choosing an alternative to Apache Airflow, it’s important to assess the specific needs of your workflows,
including the level of technical expertise within your team, the complexity of your data pipelines, and your
existing infrastructure. Whether you’re looking for a cloud-native solution, a low-code interface, or a tool
that simplifies machine learning workflows, these ten alternatives offer a wide range of features to suit
diverse use cases. By partnering with a reliable Cloud development company, you can
ensure that these solutions are tailored to meet your specific business needs and drive efficient outcomes.

From the intuitive interface of Mage and the machine learning focus of Metaflow to the robust scalability of
Flyte and the event-driven architecture of Argo, each tool addresses specific gaps in Airflow’s functionality,
helping you orchestrate data workflows more efficiently and effectively in 2024.

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