Understanding Airflow Alternatives: The Beginner’s Guide
For most companies, their ability to be competitive, stay relevant and make informed decisions comes down to how they are understanding the data they have on hand to guide these decisions. The challenge is that data is not just readily accessible. Data stacks can be improved, silos can be drained into warehouses, lakes, and lake houses, but this all takes a great amount of skill and effort.
Once the data has been transformed, transported, refined, and stored for use, then the challenges of using the data come into play. Reverse ETL and operational analytics push the data out to the company in ways that are meaningful, but this is again just one part of the process. Because of the complexity of these challenges, the ability to automate and orchestrate data pipelines is crucial.
Is Orchestrating Pipelines Important?
Finding the right kinds of tools to help data teams build out effective, functional pipelines that leverage their time wisely is one of the most important aspects of building a data team. Because the process of data mining, enrichment, and analytics is so involved, finding a way to help your team orchestrate effectively is a top priority for many companies.
While it is true that the modern data stack continues to evolve, grow and become more robust and useful, the challenge of integrating pipelines is still not solved. This data has to be able to move between sources in order to effectively achieve company goals. The amount of time and attention that this takes when using pipelines that constantly need maintenance can be counterproductive to a company’s goals.
The point of finding software that helps to provide pipeline orchestration, working with pipelines that are custom and don’t always fit ETL workflow, is to leverage time. Leveraging a data team's time wisely can have a big impact on a company’s overall data performance.
What is Airflow?
One of the most well-known names in the game of data orchestration is Airflow. Airflow is currently an Apache project, which means that it has a rich customer support system, and can be used by most data teams. One of the main ways that Apache Airflow works is by using DAGs to schedule jobs across serves and nodes.
Because Airflow is open-sourced and backed by the Apache Foundation, thousands of companies use this tool because it does represent a safe choice. The substantial user interface, along with the rich customer support that can be offered by the Apache Foundation make this a great choice for a lot of companies. However, that’s not to say Airflow is free of problems.
In fact, understanding Airflow alternatives can be a great way of making sure your data team is getting the orchestration software they need. Each team is unique, and Airflow may have certain characteristics that appeal to a large audience, but it’s not a one-size-fits-all.
One of the biggest problems that some teams may find with Airflow, is that it can tend to not be accommodating to custom, complex pipeline integration. Airflow is one of the most popular names in the game, largely because it’s been one of the very few when it comes to orchestration tools. However, the world of data technology continues to grow and move at a breakneck speed.
Issues like local development, testing, and storage abstractions, irregularly scheduled tasks, or movement between data stacks and dynamic, parameterized workflows have been a challenge for Airflow. Here are some alternatives to consider.
What Are the Leading Airflow Alternatives?
Two of the leading alternatives for Airflow are Dagster and Prefect. Both of these tools have been more recently developed with more modern, complex pipeline systems in mind. Both of them tackle problem areas of Airflow.
Dagstar Focuses On Local Development and Testing
Dagster, developed in 2018 by Jeremiah Lowin, can handle local development and testing in ways that Airflow struggles to accommodate. It links data resources to push the data needed and frees up your functions by accepting inputs and outputs that are configured at run-time.
Perfect Focuses on Scheduling Tasks
Airflow can struggle in the area of off-scheduled tasks that can cause problems with the amount of DAGs that are being used. Prefect offers a solution to modern data teams who are looking for a Flow that can be run dynamically, at any time with confidence. The way it does this is by seeing workflows as standalone objectives.
Dagstar also offers advancements in this area along with customization and modification to specific job schedules.
Conclusion
Apache's Airflow is one of the most trusted names in the business thanks to the good user interface, and rich customer care from an established company; however, it may not always meet the needs of modern data teams. Dagster and Prefect could be a good solution for more complex, pipelines that need better automation and scheduling. This field is still developing, however, and competition will drive all software providers to develop more well-rounded and useful tools.
