6 Best Airflow Alternatives for 2024

Although Apache Airflow is widely used for data workflow management, users often encounter challenges that prompt them to explore alternatives. Common grievances from both technical professionals and business data pros include inadequate documentation, a steep learning curve, and the complexities of production setup and maintenance. In this blog post, we'll delve into alternatives to Airflow that tackle these issues, offering a more user-friendly and efficient experience in managing workflows.

Shipyard

Introducing our first pick in the Airflow alternatives lineup: Shipyard. While we may be a bit biased, we genuinely believe that Shipyard offers a stark contrast to Airflow. It's not just easy to use and quick to deploy; it's designed for folks of all technical backgrounds and doesn't confine you to Python. Feel free to test and launch your Shipyard workflows right in your local environment.

What sets Shipyard apart? It's the data orchestration tool that delivers unparalleled breadth and depth for your investment. With features like organized projects, integrated notifications, error-handling, automated scheduling, on-demand triggers, and no proprietary code constraints, Shipyard stands out. The platform even throws in shareable, reusable blueprints, scalable resources for each solution, and detailed historical logging, fostering seamless collaboration and efficient resource management.

You're not boxed in either. Choose from our pre-built blueprints, tweak the code to suit your needs, or go all-in and code within Shipyard using Python, Bash, or Node.

To top it off, Shipyard offers a sleek UI for effortless management, along with robust admin controls and permissions. This ensures you maintain the reins over your projects with ease. Discover a whole new level of data orchestration with Shipyard.

Check out Shipyard:

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Prefect

Prefect is a data flow automation platform that empowers users to orchestrate Python code through its Orion engine. It also supports type annotations, async operations, and first-class functions.

With Prefect's UI, you can configure notifications, schedule workflows, and review run history. The orchestration and execution layers of its open-source sibling (OSS) can be independently managed, providing flexibility.

Explore Prefect's library, complete with predefined tasks for executing shell scripts, managing Kubernetes jobs, and even sending tweets. Prefect also boasts a community of engineers and data scientists.

This tool also facilitates parallelization and scaling through Kubernetes and supports event-driven workflows. However, it's worth noting that the limited free tier and the intricacies of deploying the self-service solution might pose challenges for some users.

In essence, Prefect emerges as a compelling option for enterprise users seeking an Airflow alternative. While it comes with a higher price tag and is best suited to the most technical such as data engineers, it offers a managed workflow orchestrator that could be a fit for your data needs.

Dagster

Dagster is built for data pros who lean towards a software engineering-centric approach to managing data pipelines. Its features include a productivity platform for defining software-defined assets, an orchestration engine, and a unified control plane for centralizing metadata.

In contrast to Apache Airflow, Dagster adopts an asset-based approach to orchestration, honing in on data asset dependencies. What differentiates Dagster from Airflow is its separation of IO and resources from the DAG logic, simplifying local testing.

However, it's essential to note that Dagster's cloud solution introduces a somewhat intricate pricing model, featuring varying billing rates per minute of compute time. While an open-source version is available on GitHub, it comes with a steep learning curve and is ideal for more complex users such as data engineers.

In short, Dagster is a solid Airflow alternative, especially for data-focused practitioners seasoned in data engineering. If you value a software engineering-oriented approach, Dagster might be your data orchestration ace.

Azure Data Factory

Azure Data Factory is a fully managed, serverless data integration service. It's designed for data teams seeking a serverless solution with robust integrations with Microsoft-specific solutions like Azure Blob Storage or Microsoft SQL Server.

Pay-as-you-go, this cloud service is designed to scale on demand, offering some degree of cost flexibility. With more than 90 built-in connectors for ingesting both on-premises and software-as-a-service (SaaS) data, Azure Data Factory is a good choice for some organization's orchestration needs.

Its integrations with the broader Microsoft Azure platform make it a worthy alternative to Airflow for those already using Azure services or seeking compatibility with Microsoft solutions.

Luigi

Luigi is a Python package designed for long-running batch processing. It provides a framework for creating and managing data processing pipelines.

Key features of Luigi are modularity, extensibility, scalability, and a technology-agnostic design. It also enables the automatic execution of data processing tasks on many objects in a batch.

Luigi can be used to stitch together various tasks like Hive queries, Hadoop and Spark jobs into a pipeline. It's best suited for backend developers looking to automate complex data flows with a Python-like solution.

While Luigi has an intuitive architecture, it has limitations. Designing task dependencies might throw you a curveball, and it lacks distributed execution capabilities, making it more appropriate for small to mid-sized data jobs.

Additionally, certain features are exclusive to Unix systems and it doesn't support real-time or event-triggered workflows, relying on cron jobs for scheduling.

Mage

Mage is built for developers, allowing them to build locally or in the cloud using Terraform, with a choice of programming languages. It also includes modular code with data validations, as Mage replaces traditional DAGs with spaghetti code.

The preview feature gives Mage users instant feedback with an interactive notebook UI. Version, partition, catalog your data produced in the pipeline. Mage also supports collaborative cloud-based development, version control with Git, and testing without waiting for shared staging environments.

Last, Mage offers built-in monitoring, alerting, and observability through its user interface, making it easy for small teams to manage and scale pipelines.

In the vast landscape of Airflow alternatives, each comes with its distinct advantages. At Shipyard, we proudly tout our platform's standout features – an intuitive design built for all technical backgrounds and lightning-fast implementation.

While we might have a soft spot for our solution, we acknowledge that every organization is unique, with its own set of needs. We encourage you to dive into the sea of options, weighing the benefits to find the platform that aligns with your team's goals and workflows.

For the hands-on experience, go ahead and sign up to try Shipyard yourself with our free Developer plan – no credit card required.