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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.

It's valuable to know where waste in your applications and infrastructure is occurring, and to have recommendations for how to reduce that waste—but finding waste isn't necessarily fixing the problem. Check out this conversation between Shashi Raina, AWS Partner Solution Architect, and Kirk Lewis, Pepperdata Senior Solution Architect, as they dispel the first myth of Apache Spark optimization: observability and monitoring.

There are several techniques and tricks when developers are tasked with optimizing their Apache Spark workloads, but most of them only fix a portion of the problem when it comes to price and performance. Watch this conversation between AWS Senior Partner Solution Architect Shashi Raina and Pepperdata Senior Solution Architect Kirk Lewis to understand the underlying myths of Apache Spark optimization, and how to ultimately fix the issue of wasted cloud resources and inflated costs.