Pepperdata Transforms the Performance of Big Data Systems at Scale

Pepperdata Transforms the Performance of Big Data Systems at Scale

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.

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Unlike other APM tools that merely summarize static data and make application performance recommendations in isolation, Pepperdata delivers complete system analytics on hundreds of real-time operational metrics continuously collected from applications as well as the infrastructure — including CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue.

The result is a comprehensive, intuitive dashboard that provides a holistic view of cluster resources, system alerts, and dynamic recommendations for more accurate and effective troubleshooting, capacity planning, reporting, and application performance management.

Pepperdata diagnoses problems quickly, automatically alerts about critical conditions affecting system performance, and provides recommendations for rightsizing containers, queues and other resources. Leveraging AI-driven resource management, Pepperdata tunes and optimizes infrastructure resources to recapture wasted capacity and get the most out of the infrastructure.

Welcome to the new world of real-time big data application and infrastructure performance management. Optimize your infrastructure, your applications, and your time — at scale.

Right now, big data is facing a visibility crisis. Workloads are increasing in scope and complexity, and developers relying on manual tuning just can’t keep up. Without observability and automation, developers can’t optimize or ensure application and infrastructure performance.

This makes keeping cloud costs in line a major challenge. According to our research, more than one-third of businesses are projected to surpass their cloud budget by up to 40%.

This overspend will only continue to skyrocket if the proper tools aren’t put in place. Pepperdata is helping customers transform the performance of their big data systems.

We provide the observability and autonomous tuning you need to recapture wasted capacity, optimize big data performance, and reduce costs.

Unlike solutions that provide only summary dashboards, Pepperdata automatically scales system resources while providing a detailed and correlated understanding of each application.

Through the constant and intelligent performance optimization of big data stacks, our customers often achieve a 3X improvement in price/performance and generate millions of dollars in savings.

Say goodbye to sticker shock and manual tuning. Say hello to the new world of big data optimization with Pepperdata.

More on the episode:
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.

Let's start with the largest cloud provider out there amazon web services offers several options for auto-scaling so you can automatically increase or decrease cloud resource usage according to parameters you set when you start up a new cluster.

This allows you to better match load and resources and saves you money auto-scaling your cloud clusters can offer significant advantages in that you don't have to pay when there's no work to do it's great for solutions where demand ebbs and flows and yet is also somewhat predictable and particularly when workloads are the most wasteful.

Looking at some of our customer workloads we have seen that yarn is relatively inefficient in being allocation-based and not usage-based and spark which is, of course, an extremely popular analytics engine for big data on yarn is also relatively inefficient.
So is auto-scaling the ultimate answer to controlling costs and waste in the cloud not necessarily this diagram shows conceptually what is going on when you enable auto-scaling on a public cloud provider...

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