Pepperdata announced that the Pepperdata product portfolio now provides autonomous optimization and observability for Spark applications running on Kubernetes.
Kubernetes is a key part of the modern hybrid, multi-cloud architecture in today’s enterprises. Spark is the #1 big data application running on Kubernetes, according to a recent survey of enterprise users. As big data applications move from Spark on legacy systems to Spark on Kubernetes, the performance of these applications can change dramatically.
Pepperdata offers full-stack observability for Spark on Kubernetes, allowing developers to manually tune their applications, while autonomously optimizing resources at run time. The combination of manual and autonomous tuning is necessary to deliver the best price and performance for these applications. Pepperdata uses machine learning across clusters, containers, pods, nodes, users and workflows to give you a complete understanding of your environment.
Pepperdata will automatically optimize Kubernetes resources while providing a correlated and granular understanding of the applications and infrastructure. Observability provides actionable information to debug and understand complex applications, and autonomous optimization ensures that the compute resources are used efficiently.
Features include:
- Autonomous optimization of resources and workloads on Amazon EKS, HPE Bluedata and Red Hat OpenShift.
- Application and infrastructure observability for Spark on EKS, Bluedata and OpenShift as well as YARN.
- A self-service dashboard so developers can manually tune using recommendations for speed or resource utilization.
- Detailed usage attribution for chargeback.
“Kubernetes is becoming increasingly important for a unified IT infrastructure, both in the cloud and the data center. Spark is the number one big data application moving to the cloud, but Spark applications tend to be quite inefficient. Optimization is key to successful implementations,” said Ash Munshi, CEO, Pepperdata. “We saw this early on with our customers, which is why we invested in the development of Spark on Kubernetes, together with Red Hat, Palantir and Google.”
The Latest
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...
On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
The mobile app industry continues to grow in size, complexity, and competition. Also not slowing down? Consumer expectations are rising exponentially along with the use of mobile apps. To meet these expectations, mobile teams need to take a comprehensive, holistic approach to their app experience ...
Users have become digital hoarders, saving everything they handle, including outdated reports, duplicate files and irrelevant documents that make it difficult to find critical information, slowing down systems and productivity. In digital terms, they have simply shoved the mess off their desks and into the virtual storage bins ...
Today we could be witnessing the dawn of a new age in software development, transformed by Artificial Intelligence (AI). But is AI a gateway or a precipice? Is AI in software development transformative, just the latest helpful tool, or a bunch of hype? To help with this assessment, DEVOPSdigest invited experts across the industry to comment on how AI can support the SDLC. In this epic multi-part series to be posted over the next several weeks, DEVOPSdigest will explore the advantages and disadvantages; the current state of maturity and adoption; and how AI will impact the processes, the developers, and the future of software development ...
Half of all employees are using Shadow AI (i.e. non-company issued AI tools), according to a new report by Software AG ...
On their digital transformation journey, companies are migrating more workloads to the cloud, which can incur higher costs during the process due to the higher volume of cloud resources needed ... Here are four critical components of a cloud governance framework that can help keep cloud costs under control ...
Operational resilience is an organization's ability to predict, respond to, and prevent unplanned work to drive reliable customer experiences and protect revenue. This doesn't just apply to downtime; it also covers service degradation due to latency or other factors. But make no mistake — when things go sideways, the bottom line and the customer are impacted ...
Organizations continue to struggle to generate business value with AI. Despite increased investments in AI, only 34% of AI professionals feel fully equipped with the tools necessary to meet their organization's AI goals, according to The Unmet AI Needs Surveywas conducted by DataRobot ...
