Pepperdata helps organizations moving to the cloud that have big data workloads with optimizing their workloads. Many organizations are facing runaway costs and there can be gaps between the expectations versus the reality of moving to the cloud. However, by utilizing autonomous optimization, the amount of resources is reduced and therefore the cost also. The autonomous optimization is further augmented with their tools for enterprise-class monitoring.
In this episode of TFiR Let’s Talk, Maneesh Dhir, CEO of Pepperdata, tells us about the company and how it is helping to tackle the challenges of monitoring big data workloads and how autonomous optimization helps reduce the consumption of resources. He discusses what sets Pepperdata apart from other toolkits like Prometheus and the key trends he is seeing in the space.
Key highlights from this video interview are:
- Many organizations are struggling to manage costs in the cloud, and Pepperdata is helping them reduce these with autonomous optimization which reduces the number of resources being consumed, hence resulting in cost reduction. Dhir explains what their company is trying to achieve and how it is tackling runaway costs.
- Dhir explains the findings from a Pepperdata survey on Kubernetes adoption. He goes into further detail on the trends he is seeing.
- Dhir discusses the role of monitoring for DevOps to ensure the environment is performant; however, not everything always goes straight forward when moving to the cloud. Costs can be higher than expected. He explains how monitoring can help make sure the app is performant, and better understand the resources being consumed.
- Observability is a big challenge for Pepperdata’s large enterprise clients who want to be able to see the key metrics, examine the behavior of their infrastructure and adjust accordingly. Pepperdata’s autonomous optimization was created to automatically adjust resources dynamically resulting in 30-40% reduction in resource consumption.
- Often when getting an application ready for the cloud, a lift and shift approach is used; however, it does not necessarily mean it is optimized and consumption can be higher than expected. Dhir tells us how Pepperdata looks at resources in real time and adjusts the workload that is being deployed for the resources that are being commissioned.
- Dhir discusses the three main areas where Prometheus falls short: not utilizing the long-term monitoring that enterprises want, the ability to do complex combinations and metrics to get the best view of the application’s behavior, and having an enterprise-class dashboard to make sense of everything and take action.
- Dhir shares his insights into what he feels the future may hold for cloud migration. He believes cost will continue to be a critical consideration for organizations. He feels that the industry needs monitoring as a foundation to get the best use of the resources that are being deployed with big data workloads, and optimizing those workloads accordingly.
The summary of the show is written by Emily Nicholls.