Why you should Adopt Modern Workflow Managers like Apache Airflow

Many teams are still relying on legacy workflow management solutions. It’s time to consider an upgrade. One of the most powerful and widely adopted modern workflow orchestration tools is Apache Airflow.

This exemplifies the diverse range of data engineering projects and software engineering practices prevalent in the industry. That is why understanding the core concepts of Apache Airflow is vital for any data engineering project involving workflow automation and migration. There’s also the DAG authoring certification, which is ideal for those in need of learning Airflow’s base concepts. A strong grasp of Airflow’s scheduling, as well as the configuration of DAGs, tasks, and their dependencies, is essential.

At Mutt Data, we have extensive experience implementing Apache Airflow solutions across industries such as finance, healthcare, and e-commerce.

Why are Legacy Workflow Systems problematic?

First, let’s define a legacy workflow system as an outdated software application or platform that organizations have used to manage and automate business processes but which may no longer meet current technological standards or business requirements. These systems often continue to operate due to their critical role in daily operations, the high cost of replacement, or the complexity involved in migrating to newer solutions. Some well-known legacy schedulers include Control-M, JAMS, Pentaho, Argo, Automic, Data Stage, Oozie, and Informatica. Legacy workflow systems often present significant challenges. These challenges begin with the duplication of workflows and components, leading to reduced efficiency. Furthermore, limited transparency and suboptimal monitoring in legacy tools make it difficult to trace failed jobs or understand dependencies.

Some other issues that stem from using legacy workflow systems are:

• Limited Scalability: Traditional cron-based workflows struggle with large-scale data processing and dynamic job execution.
• Dependency Management: Legacy schedulers require a lot of work dealing with configurations to manage dependencies across jobs and systems.
• Minimal Extensibility: Many older tools can’t be integrated easily with modern cloud services, data platforms, and APIs.
• High Licence Costs: Legacy systems often require expensive licenses and infrastructure, making them costly to maintain compared to open-source alternatives like Airflow.
• Poor Error Handling & Logging: Debugging issues in legacy systems is painful, often requiring sifting through multiple log files scattered across servers.

The evolution of scheduling tools illustrates a clear shift from rigid, opaque systems to more flexible, observable, and developer-friendly solutions. In the early days, process automation relied on system-level configurations like cron jobs—pre-installed but extremely limited, lacking proper logging, monitoring, or visual interfaces. As needs evolved, teams began using database engines or homegrown scripts to trigger jobs based on specific logic, but these still lacked orchestration capabilities and clarity. Legacy schedulers emerged to fill that gap, providing visual interfaces and centralized control, but they came with drawbacks: they were often proprietary, expensive, and difficult to integrate.

Apache Airflow incorporates reusable components, a best practice for software engineering, enhancing efficiency and reducing redundant work. Additionally, with Airflow’s open-source, teams have full access to the source code, providing greater control and the ability to customize workflows to specific organizational needs.

Transitioning to Apache Airflow (or a similar modern workflow manager) can transform how your workflows are orchestrated. Astronomer themselves have an open source repository where you can get a quick demo of a public translation with Orbiter.

Tools like Orbiter enable the transition from these legacy systems to modern schedulers by reading the source code or configuration files (often in formats like JSON) and allowing developers to define translation rules. This simplifies migrations and eliminates the manual work of decoding how scripts were scheduled and executed. Today, modern schedulers like Apache Airflow—whether deployed via Astronomer, on Kubernetes, or on AWS—represent the latest evolution: open-source, flexible, cloud-native, and built for scale.

Why should you use Apache Airflow to manage your workflows?

Apache Airflow is an open-source workflow management platform designed to programmatically author, schedule, and monitor workflows.

Here’s why it has become the go-to tool for data engineers:

1. Strong Community & Enterprise Support

Apache Airflow has a strong open-source community that continuously improves the platform with new features and enhancements. Additionally, tools like Cosmos, Starship, and Dag-factory among others, to streamline workflow orchestration and enhance Airflow capabilities:

• Cosmos – A framework to easily manage and run dbt models as Airflow tasks.In addition to its robust community. Beyond that, Cosmos also allows for reusing Airflow components like connections to run queries. It provides the flexibility to specify where debt models will run externally, such as on AWS ECS, GCP, Azure, or other cloud services. This capability supports a more modular and scalable approach to workflow execution, ensuring that teams can leverage their existing cloud infrastructure efficiently. We, at Mutt Data, have collaborated on a repo that introduces an AWS ECS operator that allows Cosmos to run dbt tasks in AWS ECS. This ensures that dbt executions are consistent with the existing Airflow DAGs running in ECS.

• Starship – A utility to migrate Airflow metadata such as Airflow Variables, Connections, Environment Variables, Pools, and DAG History between two Airflow instances.

• dag-factory – An open-source tool that simplifies the creation of DAGs dynamically.

2. Cloud-Native & Hybrid Deployments

Airflow seamlessly integrates with cloud-native ecosystems and can be deployed on Kubernetes, AWS MWAA, Google Cloud Composer, Astronomer, or self-managed setups, offering flexibility for different infrastructure needs.

Astronomer, as a managed Airflow service, provides an enterprise-grade platform that simplifies deployment, monitoring, and scaling, making it an excellent choice for teams looking for a fully managed solution with professional support.

At Mutt Data, we’ve successfully deployed Airflow across all these platforms and self-managed environments. That said, we often recommend Astronomer as our preferred option for its enterprise-grade features and seamless developer experience.

3. Airflow provides tools for everyone to use it and hears its users

For teams migrating from legacy workflow managers to Airflow, their Definitive Guide to DAGs can help refine and improve DAG implementation. For non-technical users, tools like dag-factory offer a way to generate DAGs dynamically, making it easier to manage workflows without deep coding expertise. The most important aspect to highlight here is that Airflow has an open source ecosystem which makes it a lot easier to integrate with other systems.

Airflow has been recognized as a great service by professionals and the community alike. The community spoke through the reviews of GitHub repos. Also, Airflow has a vibrant community that seeks to support users and developers, help with tool development, and enrich the use of Airflow all around. Astronomer is part of that community, and they’re a big player when it comes to expanding that community. You can see an example in their latest survey where 93% out of 5,250 surveyed say that Airflow is important for their business.

Why do we, love Apache Airflow at Mutt Data?

Airflow has been at the core of our consulting practice at Mutt Data for over 6 years. Our journey has shaped our expertise in modern workflow automation, so much so that we’ve developed best practices and refined methodologies for implementing and optimizing Airflow at scale. We decided to write about it and share what we’ve learned: Modern Workflow Management with Airflow. Our experience’s outcome is that we strongly recommend Apache Airflow as your workflow management tool due to its flexibility, scalability, and vast ecosystem. You may need help on your journey so if you're looking for a managed service with professional guidance, Astronomer provides enterprise-grade Airflow support to help teams implement best practices and optimize their workflow orchestration.

At Mutt Data, we love Apache Airflow because it empowers our data teams to streamline complex workflows with ease. Its Python-based approach and robust community support mean we can quickly adapt workflows to meet diverse client needs, whether they’re on cloud or private servers. By providing out-of-the-box features such as automatic retries, detailed monitoring, and seamless version control, Airflow helps us maintain reliability and transparency. Its scalability ensures we’re prepared for growing workloads, and its integration with various platforms and tools makes it an essential part of our data solutions. Airflow not only saves us time but also allows us to deliver greater value to our clients, which is why we rely on it so heavily in our operations.

Moreover, Astronomer plays a pivotal role in supporting the Apache Airflow community, fostering a vibrant ecosystem where data professionals can connect and grow. With over 33,000 members on Slack and more than 2,500 contributors, the Apache Airflow community offers a wealth of knowledge and resources.

Astronomer enhances this experience by providing managed Airflow services, simplifying infrastructure management, and offering scalability and elasticity. For organizations seeking a more robust, self-managed solution, Astronomer’s platform delivers high availability, fault tolerance, and dedicated support, allowing teams to focus on building and optimizing data workflows without the operational overhead.

So you’re thinking about migrating to Airflow?:

Here are the Resources to keep in mind

Experts: Mutt Data <> Astronomer

Mutt Data helps organizations migrate to Apache Airflow efficiently. As an official partner of Astronomer, we bring deep expertise in workflow orchestration, assisting teams in migrating from legacy systems. Our services include workflow assessment, DAG optimization, custom plugin development, and performance tuning—ensuring a seamless transition tailored to your business needs. Migrating to Airflow is a crucial step for many organizations. Astronomer has provided several resources to guide teams through this transition, including:

• Webinar: Moving from Legacy Schedulers to Airflow –An upcoming webinar next month that will provide a deep dive into the migration process using Orbiter.
• Airflow Summit Talk on Orbiter – A past session that covered how Orbiter facilitates migration and key lessons learned.

A Game-changing Tool: Orbiter

Suppose you’re considering migrating to Airflow and looking for a more robust, self-managed solution. In that case, Orbiter is a standout option that provides a comprehensive set of features for managing and orchestrating data workflows.

Astronomer has also developed Orbiter, a tool that simplifies the migration process from legacy task schedulers to Apache Airflow, ensuring a smooth transition with minimal downtime.

Astro’s advanced features enhance team productivity by streamlining the creation, execution, and monitoring of workflows, allowing data teams to focus on strategic tasks rather than infrastructure management. With a vibrant open-source community and enterprise-grade support from companies like Astronomer, Airflow offers the scalability, reliability, and tools necessary to efficiently manage complex workflows, making it a superior choice for data orchestration.

For teams migrating from legacy workflow managers to Airflow, Orbiter provides a structured migration path. Orbiter exports baseline code equivalent to existing logic in legacy systems, providing a strong starting point for refining workflows. While not a perfect solution, it significantly accelerates the migration process.

Key benefits of using Orbiter for migration:

• Automates Workflow Conversion: Reduces manual effort by exporting DAGs from legacy schedules.
• Ensures Logical Consistency: Helps maintain workflow dependencies during migration.
• Speeds Up Adoption: Provides a solid starting point that can be further optimized for Airflow best practices.

There’s also a point to be made about Astronomer’s prices. Astronomer’s pricing model offers a flexible, usage-based structure. This pay-as-you-go approach allows organizations to scale their data orchestration needs.

EDIT: as the moment of writing this blog, Astronomer came up with an even more flexible approach to pricing making the platform easier to access.

How to Migrate from Legacy Systems to Airflow

1. Set up networking, infrastructure, and authentication: Establish the foundational infrastructure for deploying Airflow. Utilizing managed services like Astronomer can simplify this process, offering scalability and reducing operational overhead. Implement canary processes to verify successful connections between Airflow and external systems, ensuring reliable integrations.
2. Identify common patterns: Analyze existing workflows to detect design patterns within legacy systems. Recognize elements such as scheduling configurations and task dependencies to create equivalent translation rules in Airflow. For example blocks XYZ may define job scheduling and need a translation rule to simulate the same behaviour. At the same time its necessary to know if any command is being executed remotely (sshoperator) and see which the composition of DAGs. It’s also possible to detect the way to detect dependencies on the legacy system and move that to Airflow code.
3. Migrate: tools like Orbiter automate the translation of legacy workflows into Airflow DAGs. Define translation rules that map legacy system concepts to Airflow constructs, facilitating an efficient migration process.
4. Translate: Develop rules for defining DAGs, their parameters and tasks, filtering out non-DAG elements, and customizing the migrated code.
5. Deploy: Test the migrated code in a development environment to validate functionality. Running the DAGs in this controlled setting allows for the identification and resolution of issues before moving to production.
6. Refactor: Apply best practices to enhance the efficiency and maintainability of the workflows. Consider implementing DAG factories for dynamic DAG/tasks generation, parameterizing DAGs for flexibility, and ensuring idempotency to guarantee consistent outputs given the same inputs.

Airflow v3

Airflow v3 'Airflow Tasks Anywhere, in Any Language', enables users to execute tasks in different programming languages seamlessly. This enhancement makes Airflow even more flexible and accessible for diverse workloads, eliminating the need for Python-only implementations.

Apache Airflow 3.0 launched on April 22nd and introduced significant enhancements to workflow orchestration. The biggest news is that Airflow 3 introduces significant enhancements, including DAG versioning, a modernized React-based UI, improved backfill support, and the ability to run tasks in multiple programming languages beyond Python, such as Go, Java, JavaScript, and TypeScript and so on. While DAGs (Directed Acyclic Graphs) are still defined in Python, this multi-language support enables tasks to be implemented in the language best suited to specific project needs, facilitating seamless integration with various APIs and libraries.

Additionally, Airflow 3.0 features a more flexible architecture that allows tasks to run in external environments, decoupling them from the main executor on the local machine. This advancement is achieved through the implementation of the Task Execution Interface (AIP-72), which facilitates Airflow’s evolution into a client-server architecture. This shift enables multi-cloud deployments and enhanced scalability, offering greater flexibility and security isolation for complex workflows. These improvements demonstrate Airflow’s commitment to adapting to the evolving needs of data teams, providing more robust and versatile tools for orchestrating intricate workflows. To stay updated on the latest changes and developments, follow the discussion on GitHub.

Conclusion

Migrating from legacy systems to modern workflow orchestrators like Apache Airflow is a game-changer. It provides improved visibility, scalability, maintainability, and extensibility, while also significantly reducing costs by eliminating expensive licensing fees. Additionally, counting on expert advice like Mutt Data and Astronomer as well as leveraging tools from Astronomer, such as Cosmos, Starship, Orbiter, and Dag-factory among others, can further streamline deployment, migration, and workflow management. Particularly, Orbiter significantly accelerates the migration process from legacy schedulers to Apache Airflow, resulting in substantial time savings. By automating complex translation tasks, Orbiter enhances the developer experience, offering greater control and improved visibility throughout the migration journey. This efficiency allows teams to transition more smoothly and focus on optimizing their workflows within Airflow. Are you currently transitioning from legacy systems to Airflow? Need expert assistance with your migration? Mutt Data can handle the entire migration process for you, ensuring a seamless transition to Airflow. Contact us through our Mutt Data contact page to learn how we can help!

How easy is it to get into Airflow? How efficient is Orbiter?

A Webinar to deepen your knowledge about the migration process using Orbiter.

Astronomer recently hosted a webinar to explore how to streamline migrations with Orbiter. In this session, Astronomer hosts Fritz Davenport and Naveen Sukumar demonstrate how Apache Airflow enables faster workflow delivery, reduces downtime, and helps scale operations with ease. This webinar serves as a great introduction to Orbiter—showing how the tool simplifies the migration process, helping you modernize your data operations effortlessly. You’ll also learn how Apache Airflow offers superior performance, reliability, and customization compared to legacy scheduling tools.