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We enhance performance of your Big Data workloads on any Hadoop Platform

  • 'Reduce Time to Analytics' has always been a prime goal of IT

  • Jumbune produces most comprehensive set of optimal configurations compared to other Big Data APM solutions

  • Quicker completion of analytical workloads, Ad-hoc queries and regular ETL has always been an aspiration

  • Improving execution time for specific workload/query is now possible

  • Jumbune employs best in class ML techniques to improve performance

Jumbune recommends most optimal configuration based on application execution logistics, resource consumption and real cluster state, blending all these with heuristics and ML algorithms. It employs best in class ML to deduce the most comprehensive set of configurations, which achieves the fastest execution time of your workloads.

optimize job
optimize job

We optimize resource utilization to just fit as per actual workload needs

In a shared resource environment like Hadoop, there is always a need to justifiable utilize the resources allocated to the application. Most of the workloads/queries utilize the default allocation of resources, which differs a lot from the actual consumption needs of the application. Default allocation results in resources to be over allocated or under allocated.

'Highly unbalanced allocation results in degraded performance of not only that particular workload but also results in degradation of concurrently executing workloads.'

Moreover, on infrastructures like Public Cloud, inefficient utilization of resources results in high running cost and lowers ROI.

Optimal configurations given by Jumbune not only improves application performance but also significantly reduces the resource demands of the workloads, resulting in most optimal utilization of resources to achieve best performance. On Public Cloud, this significantly reduces operating cost of Hadoop infrastructure, changes the choice of instance types and increase overall ROI.

We ensure that Hadoop deliveres to your defined workload SLAs

  • Meeting the SLA on a shared infrastructure has always been a challenge

  • Re-executions, Hit and Trail, increasing hardware horsepower has been the employed mechanisms to meet SLAs in the past

  • Workloads running together with other demanding workloads makes achieving SLAs more infeasible

Jumbune considers several factors into consideration like memory, compute power, memory buffers, Disk I/O, congestion and many others along with realistic state of other workloads on cluster to output the recommendations which will always stand to your SLA expectations. In numerous scenarios, Jumbune redefines SLAs with faster versions.

optimize job