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:
Complete this form to request a demo
[preEmptive_forms id=”52″]
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 avoids wasted/underutilized resources by recapturing them and helps with greater cost avoidance.
This novel non-uniform resource allocation strategy further reduces the cost to run elastic clusters.
Free access to Qubole for 30 days to build data pipelines, bring machine learning to production, and analyze any data type from any data source.
See what our Open Data Lake Platform can do for you in 35 minutes.