Airflow vs. Prefect vs. Dagster: Choosing the Right Data Orchestrator for Your Project

Choosing the right data orchestrator depends on your team’s needs, infrastructure, and goals. Here’s a quick overview of the three most popular tools - Airflow, Prefect, and Dagster - and where they excel:

• Airflow: Best for handling complex workflows in established systems. It’s Python-based, offers a vast ecosystem of integrations, and suits teams experienced in managing batch processes. However, it has a steep learning curve and higher operational overhead.
• Prefect: Ideal for agile teams and startups. It simplifies workflow creation, offers strong failure handling, and reduces infrastructure costs with elastic scaling. Prefect is great for dynamic, event-driven workflows but has limited data lineage features.
• Dagster: Focuses on data assets rather than tasks, making it perfect for teams prioritising data quality and lineage tracking. It integrates well with modern tools like dbt and supports cloud-native setups. However, it’s newer and has a smaller community compared to Airflow.

Quick Comparison:

Each tool has its strengths. Airflow is reliable for established setups, Prefect is simple and flexible, and Dagster offers modern features for data-driven teams. Pick the one that aligns with your infrastructure and project goals.

Airflow vs Dagster vs Prefect | Which Workflow Platform Will Streamline Your Operations in 2025?

1. Airflow

Apache Airflow has cemented itself as a trusted tool for data orchestration, initially developed by Airbnb in 2014 and released as open-source in 2015. This Python-based platform is a favourite among organisations in Australia, especially those managing intricate batch workflows. Below, we delve into its design, scalability, and integration features that make it an essential part of modern data workflows.

Airflow uses Directed Acyclic Graphs (DAGs) to represent workflows, offering a clear and logical way to visualise data pipelines [10, 11]. This structure helps data teams in Australia map task dependencies and understand the flow of data across different stages. As Vinayak Mehta, Former Data Engineer at Atlan, aptly explains:

"To oversimplify, you can think of Airflow as cron, but on steroids."

Workflow Design and Development Experience

Airflow stands out for its flexibility in creating dynamic pipelines. Unlike static tools, it allows developers to programmatically generate pipelines that can adapt to changing conditions, database queries, or configuration files [10, 12]. Its modular architecture - comprising components like a metadata database, web server, scheduler, executor, workers, and DAGs - creates a solid foundation for orchestration.

The platform's reliance on Python operators enables seamless task execution across cloud platforms and third-party services, making it highly relevant for Australian enterprises [10, 12]. For workflows that require templates, Airflow incorporates the Jinja templating engine, providing built-in parameters and macros to simplify pipeline creation [10, 12].

Scaling Capabilities and Performance

One of Airflow's standout features is its ability to scale with ease. It uses a message queue system to enable horizontal scaling as data demands grow [12, 13]. Depending on infrastructure preferences, organisations can choose between executors like Celery for fixed workers or Kubernetes for pod-based task execution. Performance tuning is also highly customisable through settings such as parallelism (default: 32), max_active_tasks_per_dag (default: 16), and scheduler_heartbeat_sec (default: 5 seconds). However, successful scaling requires careful monitoring to avoid bottlenecks. This scalability has made it a popular choice for enterprise-level operations.

Real-World Enterprise Adoption

Airflow's effectiveness is evident in real-world applications. For instance, Ford Motor Company’s Advanced Driver Assistance Systems team handles over 1 petabyte of lidar, radar, video, and geospatial data weekly, managing more than 300 concurrent workflows. This setup has significantly reduced errors and boosted reliability for their AI model development. Similarly, Northern Trust, which oversees $1.5 trillion in assets, revamped its financial warehouse systems using Airflow to handle critical data for balance sheets, income statements, and other financial reports.

Integration Ecosystem and Extensibility

Airflow's extensive integration capabilities are another reason for its popularity, particularly in hybrid and multi-cloud environments. It supports numerous plug-and-play operators, making it easy to connect with services like AWS, Azure, databases, messaging systems, and analytics platforms [10, 12]. This flexibility allows Airflow to orchestrate a wide range of tasks, from data processing to machine learning model training and even system maintenance.

The platform's web-based UI offers detailed monitoring and management tools, giving teams full visibility into workflow execution and task dependencies [10, 12]. Its extensible architecture, which supports custom operators and hooks, allows teams to tailor the platform to their unique needs. This makes Airflow a practical solution for handling diverse data workflows across industries in Australia.

2. Prefect

While Airflow has long been a go-to for established, large-scale orchestration, Prefect offers a more flexible and cost-conscious alternative designed for modern workflows. Prefect is a Python-native orchestration tool that can transform any function into a task without the need for rigid frameworks. This adaptability has made it particularly appealing to Australian data teams seeking developer-friendly tools that can still meet enterprise-level demands.

Workflow Design and Development Philosophy

Prefect simplifies workflow creation by allowing developers to turn any Python function into a task with a simple decorator. Markus Schmitt highlights this advantage:

"It addresses many of the pain points common to more complicated tools like Airflow. Specifically, Prefect lets you turn any Python function into a task using a simple Python decorator."

This simplicity translates into faster development cycles. Some users report that workflows which previously took two to three months to build with traditional tools can now be completed within a month using Prefect. Another user shared how their data engineering platform shifted from being a bottleneck to a strength, saving significant time in designing DAGs.

Dynamic Infrastructure and Cost Management

Prefect's infrastructure model dynamically adjusts resources based on actual demand, steering away from the inefficiencies of static resource allocation. This approach often leads to considerable cost savings. For instance, LiveEO cut its AWS expenses by 63% after adopting Prefect. Tony Rahloff also noted:

"After implementing Prefect, we quickly saw improved developer experience, velocity, and resilience. We were able to triple our development speed with Prefect – resulting in faster development, bug fixes, and product iterations – which also improved our time-to-customer value."

Endpoint saw similar success, reducing infrastructure costs by 73% while launching 78 new pipelines in just one quarter. Their engineers also reported being three times more productive. Across various organisations, Prefect has been shown to reduce infrastructure costs by 60–70% and cut pipeline failures by 80%.

Enterprise Integration and Scalability

Prefect's hybrid execution model allows workflows to run anywhere, all while maintaining centralised oversight. The platform includes native integrations with data warehouses, business intelligence tools, and cloud services, making it an excellent choice for streamlining ETL processes. Major names like the Washington Nationals and Cash App have adopted Prefect for their data operations. Lee Mendelowitz from the Washington Nationals shared:

"With Prefect we can define our workflows precisely, using code that's under version control. Features like tasks, task dependencies & retries, and mapping make it easy to write robust data imports and data pipelines."

Prefect's push work pools further enhance cost efficiency by scaling resources down to zero when no tasks are running. This serverless capability, combined with support for event-driven workflows, makes it an excellent fit for modern data architectures where responsiveness and resource efficiency are key.

Monitoring and Operational Excellence

Prefect provides built-in monitoring tools, including detailed logging, error handling, automated backups, and real-time event tracking. Chris Jordan praised these features:

"We have been able to stay on top of the data flows we've moved to Prefect easily. Seeing failures, successes, and outages in a timely and clear fashion has let us inform stakeholders what's up with the data flows."

Additionally, Prefect eliminates the need for intermediate storage by enabling tasks to communicate directly in memory. Its user-friendly web interface offers teams clear insights into workflow execution, reducing operational complexity and improving efficiency.

3. Dagster

Dagster takes a fresh approach to data orchestration by prioritising data assets over tasks. This shift in focus, combined with core software engineering principles, makes it a strong choice for teams looking to build scalable and maintainable data systems.

Software-Defined Assets and Developer Experience

At the heart of Dagster's design is its asset-first approach. Instead of concentrating on isolated tasks, it allows teams to define the data assets themselves, with the orchestration logic handled by Dagster. This brings principles like SOLID - common in software engineering - into the data world, making workflows easier to manage and test.

One data engineer, known as pcgamerwannabe, shared their perspective:

"Dagster's Software defined assets plus the nice way it's coded will literally revolutionise the way your team works with its data."

Enterprise Performance and Scalability

Dagster is built to handle the needs of complex enterprise environments, offering support for cloud-native, hybrid, and multi-tenant setups. A Forrester TEI study highlighted its impact, reporting US$1.7 million in faster time-to-value over three years when using Dagster+ Pro.

Real-world examples show how Dagster transforms operations. In July 2025, Clippd cut out over eight hours of manual data work each week and shifted from opaque pipelines to an organisation-wide data platform. This change enabled automated data processing for over 200 college golf programs, giving the entire team easier access to insights. Similarly, Belgium's Fédération Wallonie-Bruxelles, which serves 4.5 million citizens, doubled its pipeline delivery speed by moving from reactive maintenance to proactive data development.

Integration Ecosystem

Dagster isn't just an orchestrator - it acts as a unified control centre for data platforms. It seamlessly integrates with a wide range of tools in the modern data and AI ecosystem, ensuring it complements existing infrastructure rather than obstructing it. As Dagster.io puts it:

"Dagster integrates with every major tool in the modern data and AI stack - so your orchestrator isn't the blocker, it's the bridge."

This robust integration network includes storage solutions like AWS S3, Snowflake, and BigQuery; compute platforms such as Databricks, Kubernetes, and Spark; and transformation tools like dbt and Airbyte. With over 11,000 stars on GitHub and contributions from more than 400 developers, Dagster continues to grow and evolve in a decentralised but coordinated way.

Operational Excellence and Observability

Dagster's declarative approach allows teams to define workflows with clear dependencies, simplifying the management of complex processes. Its observability features - like lineage tracking, an asset catalogue, data validation, and testability - enhance oversight and streamline debugging. Combined with its hybrid deployment model, these features help organisations balance innovation with governance, while also speeding up the journey from development to production.

This blend of thoughtful design and operational insight makes Dagster a compelling choice for modern data teams.

Advantages and Drawbacks

The following table summarises the key strengths and limitations of each orchestrator, based on tool-specific analyses, with a focus on their suitability for Australian projects.

Beyond these core features, other factors like integration, cost, and scalability play a significant role in choosing the right tool. Each platform has unique integration strengths: Airflow shines with its vast provider ecosystem, Dagster offers seamless dbt alignment through its asset graph model, and Prefect's pure-Python approach simplifies data connectivity.

Cost considerations are particularly relevant for Australian teams, influenced by market dynamics and project scale. Airflow is fully open source, but its operational overhead can lead to higher DevOps expenses. Managed options like Amazon MWAA and Google Cloud Composer are available to offset this. Prefect provides free usage for teams with fewer than three users, with paid plans starting at approximately A$1,200 per month. Dagster offers an open-source core, with cloud tiers priced based on compute hours and asset runs.

Team structure also impacts tool selection. Larger, established organisations with diverse workloads and strict change-management policies often prefer Airflow. Data-focused teams that value lineage tracking, modular asset graphs, and testing - especially those invested in dbt - tend to favour Dagster. Startups, on the other hand, often lean towards Prefect for its simplicity and ability to support rapid iteration without requiring stateful service maintenance.

Scalability is another critical factor. Airflow's CeleryExecutor allows horizontal scaling but can struggle with high DAG-parse times. Dagster uses gRPC to isolate user-code processes, reducing scheduler load. Prefect's agent model enables elastic scaling by treating each flow run as an independent container or process.

Final Recommendations

When choosing a data orchestrator, it's essential to align your selection with your team's specific needs, technical setup, and long-term priorities. Here's a breakdown of practical advice tailored for Australian technical teams based on our analysis.

Airflow is a solid choice for organisations with established systems handling static workloads. Its reputation as an industry standard makes it ideal for teams working with traditional data infrastructures that demand reliability and scalability. If your team values a tried-and-tested solution for managing complex workflows, Airflow fits the bill.

For teams prioritising data asset management, Dagster stands out. Its asset-first approach and user-friendly interface provide a clear and comprehensive view of your data landscape. This makes it particularly advantageous for cloud-native environments or teams leveraging tools like dbt. If you're managing machine learning pipelines or analytics workflows, Dagster’s built-in testing features could prove invaluable.

Meanwhile, Prefect shines in scenarios requiring strong failure handling and event-driven pipelines. It's a great option for startups and agile teams operating with limited DevOps resources. Prefect's ability to handle dynamic workflows without the complexity of maintaining stateful services makes it an excellent fit for teams that need flexibility.

When deciding, consider your team's expertise and how well each tool integrates with your existing infrastructure. For example, teams already familiar with Airflow might face higher transition costs if switching to another platform. On the other hand, Dagster’s streamlined approach could be beneficial for those prioritising local development and testing. While Airflow works seamlessly with traditional setups, Dagster often delivers better results in modern, cloud-native environments.

It's also important to assess the total cost of ownership, factoring in infrastructure and operational overhead. While all three tools offer open-source versions, the associated maintenance and subscription-based features can vary widely.

Define your requirements - whether batch processing, real-time workflows, or machine learning pipelines - and choose the tool that aligns best. By weighing factors like onboarding speed, compatibility with your tech stack, scalability, and future growth, you can make a decision that supports your organisation’s goals effectively.

FAQs

How can I choose the best data orchestrator for my team's needs and technical setup?

To find the right data orchestrator for your team, start by examining your workflows. Are they batch-based, streaming, or event-driven? Also, think about the volume and speed of your data. These factors will help you narrow down options that align with your operational needs.

Next, take a close look at your current tech stack and assess your team's comfort level with relevant tools and programming languages like Python, which is widely used with platforms such as Airflow. Compatibility with what your team already knows can save time and reduce the learning curve.

Other key aspects to weigh include scalability, ease of use, integration options, and cost-effectiveness. Don’t overlook the importance of community support and detailed documentation - both can make a big difference when it comes to adoption and troubleshooting. By aligning these factors with your project’s unique requirements, you’ll be in a strong position to choose an orchestrator that fits your team’s goals.

How do Airflow, Prefect, and Dagster compare in terms of scaling and managing costs for data workflows?

Airflow is a strong choice for managing large and complex workflows, but its reliance on static infrastructure and external storage can make scaling up more resource-intensive and expensive. Prefect takes a different approach, designed for dynamic setups with features like in-memory data flows and smooth integration with tools like Dask or Ray. This makes it a more cost-efficient and flexible option, especially in cloud-native environments. Meanwhile, Dagster stands out with its modular architecture and a clear focus on data quality, making it a great fit for modern, scalable data-driven applications.

To put it simply: Airflow works well for static, large-scale workflows, Prefect shines in dynamic and cloud-focused environments, and Dagster brings a data-first approach with an emphasis on scalability and quality.

How does Dagster's asset-first approach improve data quality and lineage tracking compared to task-based orchestration?

Dagster takes an asset-first approach, treating data as individual, manageable assets rather than just focusing on tasks. This perspective brings greater clarity to data lineage, tracking where each asset originates, how it’s transformed, and what dependencies it has. By integrating quality checks directly into the assets, it ensures data consistency and reliability throughout the pipeline.

Unlike task-based orchestration, this method makes troubleshooting more straightforward, builds confidence in data processes, and provides a more transparent view of how data moves through the system. The outcome? Pipelines that are not only more dependable but also maintain higher standards of data quality and lineage tracking - helping you meet project goals while keeping operations running smoothly.