Running Distributed Computing Jobs using Prefect

Prefect

Prefect is an open-source workflow management system that allows you to build, schedule, and monitor data workflows. It enables you to transform any Python function into a unit of work that can be observed and orchestrated. Prefect can be used for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more. It has a dynamic engine and ephemeral API that makes it easy to run workflows interactively during the building phase. Prefect also offers the ability to cache and persist inputs and outputs for large files and expensive operations, improving development time when debugging.

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 Prefect are one of the primary means by which companies leverage distributed computing in production. Prefect offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Prefect; 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.