|
By Pepperdata
Optimizing data-intensive workloads typically takes months of planning and significant human effort to put cost-saving tools and processes in place. Every passing day increases the risk of additional expenditures—outlays that cost the business money and time, and that cause delays to new revenue-generating GenAI or AgenticAI projects. Remove the risk from optimization with Pepperdata Capacity Optimizer’s 100% ROI Guarantee.
|
By Pepperdata
In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: Bonus Myth: I’ve done everything I can. The rest of the application waste is just the cost of running Apache Spark. Unfortunately, many companies running cloud environments have come to think of application waste as a cost of doing business, as inevitable as rent and taxes.
|
By Pepperdata
In this blog series we’re examining the Five Myths of Apache Spark Optimization. The fifth and final myth in this series relates to another common assumption of many Spark users: Spark Dynamic Allocation automatically prevents Spark from wasting resources.
|
By Pepperdata
In this blog series we’ve been examining the Five Myths of Apache Spark Optimization. The fourth myth we’re considering relates to a common misunderstanding held by many Spark practitioners: Spark application tuning can eliminate all of the waste in my applications. Let’s dive into it.
|
By Pepperdata
In this blog series we are examining the Five Myths of Apache Spark Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Spark users: Choosing the right instances will eliminate waste in a cluster.
|
By Pepperdata
In this blog series we’ll be examining the Five Myths of Apache Spark Optimization. (Stay tuned for the entire series!) If you’ve missed Myth #1, check it out here. The second myth examines another common assumption of many Spark practitioners: Cluster Autoscaling stops applications from wasting resources.
|
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
Spark Dynamic Allocation is a useful feature that was developed through the Spark community’s focus on continuous innovation and improvement. While Apache Spark users may believe Spark Dynamic Allocation is helping them eliminate resource waste, it doesn’t eliminate waste within applications themselves. Watch this video to understand SDA's benefits, where it falls short, and the solution gaps that remain with this component of Apache Spark.
|
By Pepperdata
Manual tuning can remediate some waste, but it doesn’t scale or address in-application waste. Watch this conversation to learn why manually tuning your Apache Spark applications is not the best approach to achieving optimization with price and performance in mind. Visit Pepperdata's page for information on real time, autonomous optimization for Apache Spark applications on Amazon EMR and EKS.
|
By Pepperdata
Cluster Autoscaling is helpful for improving cloud resource optimization, but it doesn’t eliminate application waste. Watch the video to learn how Cluster Autoscaling can't fix the entire issue of application inefficiencies, but how Pepperdata Capacity Optimizer can enhance it and ensure it utilizes resources accordingly.
|
By Pepperdata
Apache Spark users may believe Instance Rightsizing eliminates cluster waste—but it cannot actionably optimize Spark apps. Learn more about how Instance Rightsizing is another myth of optimizing Apache Spark workloads for peak efficiency.
|
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
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
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
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.
- November 2024 (1)
- August 2024 (3)
- July 2024 (2)
- June 2024 (5)
- May 2024 (3)
- April 2024 (1)
- March 2024 (2)
- January 2024 (2)
- November 2023 (4)
- October 2023 (1)
- September 2023 (3)
- August 2023 (3)
- July 2023 (1)
- June 2023 (5)
- May 2023 (1)
- April 2023 (1)
- March 2023 (2)
- January 2023 (1)
- December 2022 (2)
- November 2022 (1)
- October 2022 (1)
- September 2022 (1)
- August 2022 (1)
- July 2022 (1)
- June 2022 (2)
- April 2022 (3)
- March 2022 (2)
- February 2022 (6)
- January 2022 (5)
- December 2021 (7)
- November 2021 (9)
- August 2021 (2)
- June 2021 (6)
- March 2021 (1)
- January 2021 (2)
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.