Spark Dynamic Allocation is a useful feature that was developed through the Spark community’s focus on continuous innovation and improvement. While Apache Spark users may believe Spark Dynamic Allocation is helping them eliminate resource waste, it doesn’t eliminate waste within applications themselves. Watch this video to understand SDA's benefits, where it falls short, and the solution gaps that remain with this component of Apache Spark.

Manual tuning can remediate some waste, but it doesn’t scale or address in-application waste. Watch this conversation to learn why manually tuning your Apache Spark applications is not the best approach to achieving optimization with price and performance in mind. Visit Pepperdata's page for information on real time, autonomous optimization for Apache Spark applications on Amazon EMR and EKS.

Cluster Autoscaling is helpful for improving cloud resource optimization, but it doesn’t eliminate application waste. Watch the video to learn how Cluster Autoscaling can't fix the entire issue of application inefficiencies, but how Pepperdata Capacity Optimizer can enhance it and ensure it utilizes resources accordingly.

Apache Spark users may believe Instance Rightsizing eliminates cluster waste—but it cannot actionably optimize Spark apps. Learn more about how Instance Rightsizing is another myth of optimizing Apache Spark workloads for peak efficiency.