Pepperdata

Santa Clara, CA, USA
2012
  |  By Pepperdata
In this blog series we’ll be examining the Five Myths of Apache Spark Optimization. (Stay tuned for the entire series!) The first myth examines a common assumption of many Spark users: Observing and monitoring your Spark environment means you’ll be able to find the wasteful apps and tune them.
  |  By Pepperdata
Cloud FinOps, Augmented FinOps, or simply FinOps, is rapidly growing in popularity as enterprises sharpen their focus on managing financial operations more effectively. FinOps empowers organizations to track, measure, and optimize their cloud spend with greater visibility and control.
  |  By Pepperdata
Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources. Spark leverages in-memory and local disk caching, along with Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources.
  |  By Pepperdata
If you’re like most companies running large-scale data intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps activities to ameliorate or address this waste and the cost it incurs, things such as: … and the list goes on. These are infrastructure-level optimizations.
  |  By Pepperdata
Gartner, Inc. estimates that worldwide spending on public cloud services is forecast to grow 20.4% to total $678.8 billion in 2024. With many organizations incorporating FinOps practices to govern how they spend their money in the cloud, Real-Time Cost Optimization is essential to saving money. In particular, as the market for Generative AI workloads continues to explode, organizations will need to consider a range of cost-savings models to extract optimal efficiency.
  |  By Pepperdata
Apache Spark versus Kubernetes? Or both? The past few years have seen a dramatic increase in companies deploying Spark on Kubernetes (K8s). This isn’t surprising, considering the benefits that K8s brings to the table. Adopting Kubernetes can help improve resource utilization and reduce cloud expenses, a key initiative in many organizations given today’s economic climate.
  |  By Pepperdata
With Kubernetes emerging as the de facto operating system of the cloud, capable of running almost anything, it’s not a surprise that many enterprises are rapidly porting their Apache Spark workloads to Kubernetes. This includes migrating Amazon EMR workloads to Amazon EKS to gain the additional deployment and scaling benefits of a fully managed service like Amazon EKS.
  |  By Pepperdata
When Apache Spark works well, it works really well. Sometimes, though, users find themselves asking this frustrating question. Spark is such a popular large-scale data processing framework because it is capable of performing more computations and carrying out more stream processing than many other data processing solutions. Compared to popular conventional systems like MapReduce, Spark is 10-100x faster.
  |  By Pepperdata
Pepperdata’s ability to halve cloud costs at top enterprises may seem radical and new, but it’s absolutely not. Pepperdata has been hardened and battle tested since 2012, and our software is currently deployed on about 100,000 instances and nodes across some of the largest and most complex cloud deployments in the world. We’re an AWS ISV Accelerate partner focused on helping customers save money running Spark on Amazon EMR and Spark and microservices on Amazon EKS.
  |  By Pepperdata
Here at Pepperdata, we’ve been on a number of sales calls lately where there’s a sense of incredulity on the other side of the video screen. How does Pepperdata extract as much as 50 percent in cost savings from some of the most sophisticated clusters in the world, the ones that had already been optimized for peak performance by the most dedicated and talented IT teams? It almost seems too good to be true. It’s not.
  |  By Pepperdata
It's valuable to know where waste in your applications and infrastructure is occurring, and to have recommendations for how to reduce that waste—but finding waste isn't necessarily fixing the problem. Check out this conversation between Shashi Raina, AWS Partner Solution Architect, and Kirk Lewis, Pepperdata Senior Solution Architect, as they dispel the first myth of Apache Spark optimization: observability and monitoring.
  |  By Pepperdata
There are several techniques and tricks when developers are tasked with optimizing their Apache Spark workloads, but most of them only fix a portion of the problem when it comes to price and performance. Watch this conversation between AWS Senior Partner Solution Architect Shashi Raina and Pepperdata Senior Solution Architect Kirk Lewis to understand the underlying myths of Apache Spark optimization, and how to ultimately fix the issue of wasted cloud resources and inflated costs.
  |  By Pepperdata
Pepperdata has saved companies over $200M over the last decade by reclaiming application waste and increasing your hardware utilization to reduce costs in the cloud. It completely eliminates the need for manual tuning, applying recommendations, or changing application code: it's autonomous, real-time cost optimization.
  |  By Pepperdata
Wondering how to get Pepperdata Capacity Optimizer implemented into your application environment?
  |  By Pepperdata
Not every application has wasted capacity in it—or do they? Watch Ben Smith, VP Technical Operations at Extole, discuss how he discovered that there's around 30% of application waste within every running app, and how Extole went about saving that wasted capacity.
  |  By Pepperdata
Watch Mark Kidwell, Chief Data Architect of Data Platforms and Services at Autodesk, explain why the company included Pepperdata as part of their core automation process for optimizing their Apache Spark applications.
  |  By Pepperdata
Pepperdata Capacity Optimizer Next Gen is the only cost optimization solution for both Apache Spark and microservices that can save you between 30–47% on your cloud bill. No matter if you try to manually tune your applications on your own, an estimated one-third of what is spent every day on cloud computing resources is wasted. While you might have cost-optimized your infrastructure with things like savings plans, spot and reserved instances, that doesn’t address the waste inherent in your applications.
  |  By Pepperdata
Learn how Pepperdata uses machine learning to provide Continuous Intelligent Tuning automatically to your Amazon EKS applications, helping your platform team recover wasted capacity and ultimately reduce your spend for cloud resources.
  |  By Pepperdata
Pepperdata Capacity Optimizer Next Gen is the only cost optimization solution for both Apache Spark and microservices that can save you between 30–47% on your cloud bill. No matter if you try to manually tune your applications on your own, an estimated one-third of what is spent every day on cloud computing resources is wasted. While you might have cost-optimized your infrastructure with things like savings plans, spot and reserved instances, that doesn’t address the waste inherent in your applications. Pepperdata is the only cost optimization solution that.
  |  By Pepperdata
With cloud cost optimization critical in establishing an innovative big data framework, AWS and Pepperdata gathered multiple experts to discuss how to maximize your savings with Amazon EMR and Pepperdata.
  |  By Pepperdata
Big data stacks are being moved to the cloud, enabling enterprises to get the most value from the information they own. But as demand for big data grows, enterprises must enhance the performance of their cloud assets. Faced with the complexity of cloud environments, most enterprises resort to scaling up their whole cloud infrastructure, adding more compute, and running more processes.
  |  By Pepperdata
There has been an ongoing surge of companies beginning to run Spark on Kubernetes. In our recently published 2021 Big Data on Kubernetes Report, we discovered that 63% of today's enterprises are running Spark on Kubernetes. The same report found that nearly 80% of organizations embrace Kubernetes to optimize the utilization of compute resources and reduce their cloud expenses. However, running Spark on Kubernetes is not without complications and problems.
  |  By Pepperdata
Increasingly, many organizations find that their current legacy monitoring solutions are no longer adequate in today's modern IT world. These enterprises find themselves struggling to manage and understand unprecedented amounts of data. With such large amounts of data needing to be dealt with, it is no wonder why it's a struggle for enterprises to leverage it for business success. Not to mention that optimizing performance and keeping costs in line is a technical challenge they must face at the same time.
  |  By Pepperdata
IT transformation projects are complex, demanding undertakings. They loop in multiple departments, and various budgetary considerations. This is a five-step guide designed to help enterprises and IT transformation teams prepare, plan, and execute their IT transformation strategy.
  |  By Pepperdata
According to Gartner, as of 2019, 35% of CIOs are decreasing their investment in their infrastructure and data center, while 33% of them are increasing their investments in cloud services or solutions.

Pepperdata offers big data observability and automated optimization both in the cloud and on premises.

As big data stacks increase in scope and complexity, most data-driven organizations understand that automation and observability are necessary for modern real-time big data performance management. Without automation and observability, engineers and developers cannot optimize or ensure application and infrastructure performance, nor keep cost under control. With support for technologies including Kubernetes, Hadoop, EMR, GCP, Spark, Kafka, Tex, Hive, and more, Pepperdata knows big data. Powered by machine learning, the Pepperata solution delivers application SLAs required by the business while providing complete visibility and insight into your big data stack.

Pepperdata helps some of the most successful companies in the world manage their big data performance. These customers choose and trust Pepperdata because of three key product differentiators: autonomous optimization, full-stack observability, and cost optimization.

Automatically optimize your big data workloads in real time with these three key features:

  • Autonomous Optimization: Pepperdata Capacity Optimizer provides autonomous optimization that enables you to reclaim resource waste with continuous tuning and automatic optimization; optimize Spark workloads with job-specific recommendations, insights, and alerts; and get an up to 50% throughput improvement to run more workloads.
  • Full-Stack Observability: Pepperdata Platform Spotlight and Application Spotlight provide big data observability, giving you actionable data about your applications and infrastructure. Understanding system behavior can transform your organization from being reactive to proactive to predictive.
  • Cost Optimization: Optimizing operational costs is critical for your business. As data volumes increase so does complexity—and the costs of processing it. Whether you are running Apache Spark, Hive, Kubernetes, or Presto workloads, Pepperdata can help your organization optimize operational costs.

Automatically Optimize Your Big Data Workloads and Control Costs on Any Cloud.