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.

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.

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.

The promise of autoscaling is that workloads receive exactly the cloud computational resources they require at any given time, and you only pay for the server resources you need, when you need them.

Monitoring big data cloud performance in the cloud with unified visibility across the entire ecosystem is critical to the success of any cloud deployment. In both the data center and cloud deployments, a proliferation of diverse performance solutions and microservice applications across infrastructures and networks can severely complicate cluster performance management. Hidden network dependencies and the complexity of managing many different solutions can negatively impact the application experience for both internal and external users.

Big data with cloud computing is a powerful combination that can transform your organization, process, and analyze your big data faster, and improve your products and business with actionable insights. Bringing your big data cluster to the cloud presents huge opportunities, but there are some challenges that need to be overcome. Is your organization really ready for the complexity of managing big data analytics in the cloud?

As corporate big data leaders look to improve data quality, turn around some of their big data projects in 2021, and optimize and improve application and cluster performance to meet business objectives, big data and analytics remain essential resources for companies to survive in a highly competitive big data environment. As you help your organization plan for the future and prepare for where big data is going in 2021, join presenter, Pepperdata Field Engineer, Kirk Lewis for this webinar.

At Pepperdata we have been analyzing many thousands of Spark jobs on many different clusters, on-prem and cloud production clusters running Spark across a variety of industries and applications, and even workload types. In this presentation, Alex and I are going to cover a very brief intro to Spark, and we'll discuss some of the common issues we have seen and the symptoms of those issues and also how you can address and overcome some issues like that without having to think too hard.