Running Distributed Computing 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.

Distributed Computing

Distributed computing technology refers to a system where multiple computers work together to solve a problem. It allows for parallel processing of data across multiple machines, which can lead to faster processing times. Distributed computing technology has become increasingly popular due to the rise of big data. It allows for the processing of large amounts of data that would be too large for a single machine to handle. Some examples of distributed computing technologies include Apache Hadoop, Apache Spark, and Apache Flink.

Open source orchestrators like Airflow are one of the primary means by which companies leverage distributed computing 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 Distributed Computing 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.