Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.