Running R Jobs using Airflow

Airflow

Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. It was first developed by Airbnb and is now under the Apache Software Foundation. Airflow uses Python to create workflows that can be easily scheduled and monitored. Airflow can help you move data from one source to a destination, filter datasets, apply data policies, manipulation, monitoring and even call microservices to trigger database management tasks. It can be used for batch jobs, organizing, monitoring, and executing workflows automatically. Airflow has been used by many companies for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more.

R

R is a programming language that was created for statistical computing and graphics. It has become increasingly popular over the years because of its flexibility, ease of use, and powerful data analysis capabilities. R has a rich ecosystem with complex data models and elegant tools for data reporting. It offers a wide variety of statistics-related libraries and provides a favorable environment for statistical computing and design. R is especially popular among data scientists, statisticians, and researchers who work with large datasets. It is used for data analysis, statistical inference, machine learning algorithms, and data visualization. R can also be used for creating reproducible, high-quality research reports.

Open source orchestrators like Airflow are one of the primary means by which companies leverage R in production. Airflow offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Airflow; this operator makes it easy to either swap to or get started with running R jobs that utilize Kaspian's flexible compute layer.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.