Community adoption of Kubernetes (instead of YARN) as a scheduler for Apache Spark has been accelerating since the major improvements from Spark 3.0 release.
Companies choose to run Spark on Kubernetes to use a single cloud-agnostic technology across their entire stack, and to benefit from improved isolation and resource sharing for concurrent workloads.
In this talk, the founders of Data Mechanics, a serverless Spark platform powered by Kubernetes, will show how to easily get started with Spark on Kubernetes.
We will go through an end-to-end example of building, deploying and maintaining an end-to-end data pipeline. This will be a code-heavy session with many tips to help beginners and intermediate Spark developers be successful with Spark on Kubernetes, and live demos running on the Data Mechanics platform.
– Setting up your environment (data access, node pools)
– Sizing your applications (pod sizes, dynamic allocation)
– Boosting your performance through critical disk and I/O optimizations
– Monitoring your application logs and metrics for debugging and reporting