Operations | Monitoring | ITSM | DevOps | Cloud

2021 Pepperdata Survey: The Reality of Kubernetes in Action

More companies than ever before are migrating to Kubernetes and seeing the results of Kubernetes in action. Kubernetes (K8s) is a key platform for big data users, and as such, we wanted to dive deeper and discover some new truths about current Kubernetes challenges and what the solutions might be. We surveyed 600 IT and big data professionals from various industries to determine which big data applications enterprises are moving or intending to move to Kubernetes.

What is Cloud Repatriation and How to Avoid It with Cloud Cost Management

Cloud computing is one of the great technologies of our era. As such, enterprises everywhere are in a hurry to migrate to the cloud. However, one of the less-talked-about trends of our time is cloud repatriation: the process of enterprises reversing their decision, leaving the cloud, and returning to an on-prem setting. According to TechTarget, 85% of enterprises reported plans of repatriating their workloads from the public cloud in 2019.

A Simple Guide to Taming the Beast That Is 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.

Lower Your Google Cloud Costs with These 5 Google Dataproc Best Practices

Thinking about using Google Dataproc as your cloud vendor? We can see why. Google Dataproc is a powerful tool for analytics and data processing, but to get the most out of it you have to ensure you use it properly. We’re going to explore five best practices you can use to lower your Google cloud costs while maximizing efficiency: Following these tips will ensure the best performance and help keep your cloud costs in line.

Pepperdata Lets AWS Auto Scaling Execute More Big Data Workloads

Here at Pepperdata, we continuously work to improve our products and better serve our customers. Whether it’s executing more big data workloads or ensuring their resource consumption remains optimal, we want our customers to get the best value and tangible benefits from our products while not overshooting their big data cloud budgets. Today, we’re bringing you the data to back up our claims that all of this is possible.

How DevOps Can Reduce the Runaway Waste and Cost of Autoscaling

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.

Learn How to Simplify Kubernetes Performance Management | Pepperdata

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.

Big Data Performance Management Solution Top Considerations

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.