Workflow Solutions with Apache Airflow
Due to the increase of automated tasks, process streams, and data integrations; modern-day companies have never needed specialized data science tools more. No matter what industry your enterprise is in, the type of AI to manage and monitor tasks throughout execution is crucial [7], and many corporations are recently leaning on Apache Airflow for just that.
Apache Airflow is an open-source workflow management platform created in 2014 by the engineers of Airbnb [6]. Workflows are defined, scheduled, and executed as Python scripts which allow complex workflows to be mapped quickly and efficiently, or used to build directed acyclic graphs (DAG) completely in Python [3].
To expand further on some of the capabilities of Apache, suppose you have an algorithm running in production; Apache Airflow can monitor this production to ensure the precision of the algorithm does not fall beneath the 90% threshold. Through scheduled evaluations, Airflow can detect if the KPI requirement is not met, and if so, automatic retraining and redeployments are initiated [2]. Without this type of tool, users would have to perform repetitive manual tasks from previous phases, resulting in slow recovery that is less cost-effective.
Apache Airflow can also be beneficial when automating data and ML pipelines allowing systems to perform ETL. Machine learning workflows tend to be more complex than ETL because of the dependencies between each step and mulitiple data sources and hardware requirements like CPU vs. GPU [4]. Utilizing the Airflow framework allows ease of implementation with fewer workflow errors.
In December 2020 Apache Airflow 2.0 was released with a more modern version including a UI with an Auto-refresh feature providing the updated status of the workflow’s progress. The latest version also includes a schedule that minimizes bottlenecks and is up to 17 times faster than in prior versions [1].
Even though data science has accelerated the success of countless enterprises, bad data is still estimated to add costs of roughly $3.1 trillion a year nationally [5]. STAND 8 can provide end-to-end solutions with reliable and experienced data scientists for the most challenging projects. Reach out today to partner with our Technical Solutions and Delivery Teams to discuss your next project and hiring needs!
Resources
- Anisienia, Anna (2021). “Is Apache Airflow 2.0 Good Enough For Your Current Data Engineering Needs?” https://towardsdatascience.com/is-apache-airflow-2-0-good-enough-for-current-data-engineering-needs-6e152455775c
- Capuano, Andrea (2020). “Orchestrating Machine Learning Experiments for MLOps Using Apache Airflow.” https://medium.com/analytics-vidhya/orchestrating-machine-learning-experiments-for-mlops-using-apache-airflow-dcbc0bab3801
- Hamilton, Ernest (2021). “What is Apache Airflow and Why Should You Use It In Your Company?” https://www.techtimes.com/articles/256141/20210120/what-is-apache-airflow-and-why-should-you-use-it-in-your-company.htm
- Lars (2021). “Apache Airflow: Machine Learning Workflows in Production.” https://www.nextlytics.com/blog/apache-airflow-machine-learning-workflows
- Monnappa,Avantika (2021). “Why Data Science Matters and How It Powers Businesses.” https://www.simplilearn.com/why-and-how-data-science-matters-to-business-article
- Naik, Kaxil (2020). “Air Flow 2.0- Planning.” https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+2.0+-+Planning
- Smallcombe, Mark (2020). “Apache Airflow: Explained.” https://www.xplenty.com/blog/apache-airflow-explained/
- Wiggers, Steef (2020). “AWS Introduces Amazon Managed Workflows for Apache Airflow.” https://www.infoq.com/news/2020/12/amazon-managed-apache-airflow/