Workload-Aware Autoscaling Reduces Data Lake Costs

Are you tired of Paying for Compute Time You Aren’t Using?

It’s time to explore Workload-Aware Autoscaling from Qubole

Thursday, June 16th, 11:00 AM CST

Can’t attend but don’t want to miss out? Register and we will send you the recording.

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.

Join us and see how 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.

To attend live, or receive a recording of this session, please register below:


PRESENTED BY:

Brian C FlǕg – Presales Solutions Architect

With decades of analytical expertise, Brian is an accomplished technologist who has achieved success in computational solutions, from supercomputing, to cluster and grid computing, to pre and post cloud computing, research, business intelligence, scientific analytics, engineering, simulations and animations, data sciences, distributed and parallelism computing.

ABOUT QUBOLE

Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytics, and machine learning with near-zero administration. Qubole is trusted by leading brands such as Expedia, Disney, Oracle, and Adobe to spur innovation and transform their businesses for the era of big data.

No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent.