Workflows are an integral part of business applications. One system needs to be able to process and send data to another one seamlessly. To do so, businesses must seek out the most practical tools available on the market. With so many available, the company you choose must have the knowledge and experience needed in this field to give your business what it needs effectively.
Hightouch provides everything data teams need to put data into action by moving it from your warehouse to the tools your business needs. Offering solutions for customer service, marketing, and sales in the Healthcare sector, Hightouch utilizes superior workflow management tools like Prefect and Dagster, which are great alternatives to products such as Apache Airflow.
Before we go jumping into the Airflow alternatives that everyone should know about, let me first summarize what Apache Airflow is and what it is used for.
Apache was created at Airbnb in 2014 as a way for the company to manage progressively compounded workflows. Apache Airflow is an open-source workflow automation tool used to set up and maintain robust data pipelines.
Airflow manages and structures ETL pipelines using Direct Acyclic Graphs or DAGs. An ETL is a three-phase process of computing, extracting, and transforming data. An ETL makes it possible for different types of data to work together. A DAG is the core concept of Airflow as it collects tasks in an organized fashion that must be completed in a specific order.
Now that we have a better grasp of Apache Airflow, let's discuss why you shouldn't use Airflow as a viable tool for your business.
It is well known that there are some issues when using Apache Airflow. With non-intuitive onboarding and scheduler, users have found it challenging to navigate. Besides the fact that there is no accurate way to track data quality, Airflow offers no versioning of data pipelines which means overwritten information each time you want to make changes and updates.
Windows users have found running Apache Airflow very challenging. A locally running platform is ideal for developers in creating workflows and scheduling and maintaining tasks. Not to mention the severe lack of data sharing between tasks, and debugging is incredibly time-consuming.
Let's begin with the ease of startup. Both Airflow and Prefect have a similar installation process using the same tools. Still, it is somewhat easier to quickstart with Prefect as it is a simple one-step operation that has extra packages that come along with it.
Although Prefect is similar to Airflow in that it is an open-source project, there is also a paid cloud version to track your workflows which is super easy to set up. Both Prefect and Apache Airflow were built using Python. Python is a computer programming language used to develop websites and software.
Unlike Airflow, Prefect can be started quickly and only needs a single agent to run the flows.
Airflow has a definite learning curve, and you need to really understand DAGs. Prefect is simply a primary Python function and is wrapped up with the flow, making operation a breeze.
In Prefect, projects organize all flows, and you can run multiple flows inside of any project. This very advantageous option is not available with Airflow, therefore leaving me to conclude that Prefect is a better recourse for your business than Apache Airflow.
Just like Airflow and Prefect, Dagster is an open-source project and can scale any workload fluidly. Dagster comes with a fully integrated scheduler with incorporated queuing for scheduled runs.
While Airflow's scheduler is adequate at running tasks, there stands a real possibility of some tasks being missed as the scheduler is known to fail at times.
The interlude between tasks initiations is higher with Airflow than with Dagster. The scheduler within Dagster is a daemon process, meaning a program that runs continuously and exists for the purpose of handling periodic service requests that a computer system expects to receive.
Parameters and assets can be passed within the functions and pipelines in Dagster, wherein Airflow, it is not easy to pass parameters to DAG - making the overall flow of data slow dramatically. Dagster creates a more systematic process that is most beneficial for businesses everywhere.
A versatile and dependable workflow management platform is vital to improving building data applications' development and testing experience. Users are not only more productive with tools such as Prefect and Dagster, but they are building exceptionally unique systems businesses large and small are relying on worldwide. Streamlining and automating repetitive tasks increase overall efficiency while decreasing the chance for error. The proof is in the pudding. Smarter decisions result in higher performance.
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