Airflow, as we know, is one of the original platforms used by data engineers for orchestrating workflows and pipelines. Today, you will see thousands of companies still using Airflow or at least some part of Airflow to manage their data pipelines. However, there have always been shortcomings with Airflow that have held data engineers back from creating efficient workflows for business tools across data systems. It’s not surprising companies were using Airflow because it was really all that existed for data engineers to work with complex data pipelines. Now, in 2022, there are Airflow alternatives that might be worth looking into if you have ever gotten frustrated by the limitations of Airflow. Things like limited testing, non-scheduled workflows, parameterization, data transfer, and storage abstraction have been common hiccups for data engineers. In this day in age, data has undoubtedly shown its worth. Using Airflow alternatives may help you make the most of your gathered data and use it to efficiently manage operations and grow a business.
Shortcomings of Airflow
Airflow has done its time and has provided data teams with enough to make companies that exist today successful. The cost may have been sleepless nights for data engineers, but still has proven to be one of the top orchestrating tools. Considering it is so widely used, if someone has ever run into a problem, someone out there has most likely come up with a solution. This tool has become a common language for most data engineers, proving to be a safe option for companies to lean on. Although, in its safety, Airflow has its fair share of problems. With it being the only real orchestration tool available for quite some time, data teams accepted their hurdles and did everything they could to make Airflow meet their increasing demands. However, its limits have created Airflow alternatives that have done their best to meet the growing demands of complex pipelines, data warehouses, data lakes, and orchestrating workflows. Some of the common issues engineers have seen with Airflow are:
- Local development, testing, and storage
- Off-schedule tasks
- Movement of data between related tasks
- Dynamic workflows
Airflow alternatives sought out to address these pain points and increase the ease of working with complex data. Although Airflow alternatives haven’t been around as long as Airflow itself, they are working to meet the demands of today’s data engineering. They might not be perfect, but they sure are trying to give data engineers a little more sleep.
Prefect’s Approach
Prefect made its debut in 2018, and was founded by Jeremiah Lowin. Lowin worked heavily with Airflow, and decided to take what he learned to create Prefect. It was created with the idea in mind that the users of Prefect do know how to code, but the goal is to make the process of taking that code and distributing it into pipelines a lot more simple. This, of course, is supported by its precise scheduling and orchestration engine. Prefect addresses local development and storage abstraction by being able to parametrize workflows using smaller datasets for local development and large data sets for production.
When it comes to scheduling, a workflow can be scheduled at any time, taking less than five seconds to run. This addresses Airflow’s limit of off-schedule tasks that don’t meet the need of Directed Acyclic Graphs (DAGs) which need a schedule to run correctly. Prefect also increases the ease of moving data between related tasks using clear inputs and outputs that seamlessly work together. In dynamic workflows, parameters can be specified, making complex computations much easier, and showing Prefect as an Airflow alternative in 2022.
Dagster’s Approach
Dagster was also founded in 2018 by Nick Schrock. Schrock built Dagster thinking about the full development lifecycle in mind, and how to simplify that process moving forward. Dagster narrows in on storage abstraction by forcing the resources that are configured at run-time to continue the data. The function is specific about data frames as inputs and outputs, producing high levels of abstraction to support local development. Dagster also allows flexibility with manual and scheduled runs. It even takes it a step further by allowing users to change a behavior based on the specific job being run. This gives the user a lot of power with orchestration. When moving data between tasks, Dagster allows for inputs and outputs to be more specific and even offers an optional type hinting system for testing. For dynamic workflows, you can define and create explicit parameters for graphs. This gives the ability to create dynamic configurations, hooks, and executors.
Your Alternative
Although Airflow has been the go-to orchestrating tool, there are Airflow alternatives available like Prefect and Dagster. These alternatives exist to address the pain points of Airflow, and provide users with a more explicit process for working with data. As the data space continually develops, alternatives will continue to develop too, making working with components of data engineering much more manageable now and in the future.
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