Stop Overpaying for Data Processing in Data Lakes
Are you tired of Paying for Compute Time You Aren’t Using?
It’s time to explore Workload Aware Autoscaling from Qubole
Downscale, upscale, and rebalance clusters automatically in the cloud based on SLA, priority, and workload context of each job.
Autoscaling is a mechanism built into Qubole that automatically adds and removes nodes so you are never running more than you need to handle the workload you have.
Qubole autoscaling automatically adds resources when computing or storage demand increases, while keeping the number of nodes at the minimum needed to meet your processing needs efficiently.
With Workload Aware Autoscaling from Qubole you can:
- Prevent cost overruns by shutting down idle nodes upon job completion.
- Get additional utilization of the existing compute nodes instead of adding additional nodes.
- Reduce the cost to run elastic clusters.
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Aggressive Downscaling
Prevent cost overruns by shutting down idle nodes upon job completion.
Use Aggressive Downscaling to rebalance workloads across active nodes and decommission idle ones without the risk of data loss. Enable faster recycling of clusters and nodes while simultaneously providing cost savings, stability, performance, and fault tolerance benefits.
Optimized Upscaling
Get additional utilization of the existing compute nodes instead of adding additional nodes.
Optimized upscaling avoids wasted/underutilized resources by recapturing them and helps with greater cost avoidance.
Workload Packing
Workload packing performs smart allocation of workloads, freeing up larger pools of nodes to downscale while preventing cluster hot spots and honoring data locality preferences.
This novel non-uniform resource allocation strategy further reduces the cost to run elastic clusters.
Infinite scale and possibilities with Qubole Data Lake
