The growing adoption of Hadoop and Spark has increased demand for Big Data and Performance Management solutions that operate at scale. However, enterprise organizations quickly realize that scaling from pilot projects to large-scale production clusters involves a steep learning curve. Despite progress, DevOps teams still struggle with multi-tenancy, cluster performance, and workflow monitoring. This webinar discusses the top considerations when choosing a big data performance management solution.

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.

Complex applications running on Kubernetes scale super fast, but this can create visibility gaps that can make detecting and troubleshooting Kubernetes issues as difficult as finding a needle in a haystack. Although Docker and Kubernetes are now becoming standard components when building and orchestrating applications, you’re still responsible for managing the performance of applications built atop this new stack.

Hi everyone. My name is Heidi Carson and I’m a product manager here at Pepperdata. Today, I’m going to share a bit about how you can tame the cost of autoscaling your cloud clusters. As you may well be aware, the incredible flexibility and scalability of the public cloud make it an appealing environment for modern software development. But, that same flexibility and scalability can lead to runaway costs when the cloud doesn't scale the way you might expect.

Pepperdata has engineered a big data APM solution that empowers operators to automatically optimize the performance and capacity of their big data infrastructure while enabling developers to improve the performance of their applications.

Do traditional on-prem observability techniques translate to the cloud? Many big data enterprises lack observability and thus struggle to manage and understand unprecedented amounts of data in the cloud. A monitoring solution may alert to a problem, but it can’t pinpoint the issue or quickly get to the root cause.