Pepperdata CEO Ash Munshi discusses Pepperdata's origin from stabilizing big data workloads to optimizing cloud costs in real time. With cost control becoming an imperative for companies with an uncertain economic outlook—as well as a difficulty in managing how they spend in the cloud—Munshi walks through how Pepperdata reduces cloud costs for companies in the Fortune 5 through autonomous and continuous resource utilization.

Running your infrastructure in the cloud can lead to wasted resources and ultimately overspending. Pepperdata Capacity Optimizer for Amazon EMR operates dynamically in real time to optimize performance without the need to model your workloads ahead of time, change application code, or platform settings. It provides autonomous optimization continuously, improving resource utilization at both the instance level and with the EMR autoscaler. Watch the full video for a deep-dive on how Pepperdata solves the problem of cloud overspending and resource waste without manual intervention.

Running your infrastructure in the cloud can lead to wasted resources and ultimately overspending. Pepperdata Capacity Optimizer for Amazon EMR operates dynamically in real time to optimize performance without the need to model your workloads ahead of time, change application code, or platform settings. It provides autonomous optimization continuously, improving resource utilization at both the instance level and with the EMR autoscaler. Watch the full video for a deep-dive on how Pepperdata solves the problem of cloud overspending and resource waste without manual intervention.

Get the Pepperdata 2021 Kubernetes on Big Data report and start your journey of better understanding how your competitors are managing their data with Kubernetes. Cloud vendors have proliferated and promised users optimal performance and tight spend. Still, many of these vendors don't provide visibility into Kubernetes big data, resulting in performance issues, poor resource allocation, overspending, and ineffective tuning. To fully optimize your Kubernetes big data, maximize performance, and reduce spend, you need to step beyond the basic K8s measurements and look at app performance.

Take a guided tour of the Pepperdata Performance Management Software. Pepperdata's big data performance management solution with a focus on managing big data and Spark on Kubernetes.

Containers are amazing. But when you start to orchestrate them in a complex environment, they can become quite the beast. Kubernetes is one of the best tools to tame that beast, but few resources exist to help you manage your big data workloads on Kubernetes. If you want to learn how you can optimize your big data workloads on Kubernetes, this is for you.

Gain the knowledge of Spark veteran Alex Pierce on how to manage the challenges of maintaining the performance and usability of your Spark jobs. Apache Spark provides sophisticated ways for enterprises to leverage big data compared to Hadoop. However, the increasing amount of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine.

Does your big data analytics platform provide you with the Spark recommendations you need to optimize your application performance and improve your own skillset? Explore how you can use Spark recommendations to untangle the complexity of your Spark applications, reduce waste and cost, and enhance your own knowledge of Spark best practices. Topics include: Join Product Manager Heidi Carson and Field Engineer Alex Pierce from Pepperdata to gain real-world experience with a variety of Spark recommendations, and participate in the Q and A that follows.

Big data analytics performance management is a competitive differentiator and a priority for data-driven companies. However, optimizing IT costs while guaranteeing performance and reliability in distributed systems is difficult. The complexity of distributed systems makes it critically important to have unified visibility into the entire stack. This webinar discusses how to maximize the business value of your big data analytics stack investment and achieve ROI while reducing expenses. Learn how to.

Autoscaling is the process of automatically increasing or decreasing the computational resources delivered to a cloud workload based on need. This typically means adding or reducing active servers (instances) that are leveraged against your workload within an infrastructure.