Stop the Costs of Big Data Projects Spiraling Out of Control

Monitor, Measure, Analyze the real cost and return of Data Lake.

Stop your compute costs from spiraling.

Join us on Tuesday, April 12th, 11:00 AM CST

Can’t join us on this date? We have another session scheduled on Tuesday 26th April, click here to register

Are the compute costs of your Big Data projects spiraling out of control? Do you need a better way to manage costs? This is a common problem our customers faced, and one that Qubole resolves thanks to its workload-aware auto-scaling capabilities.

Qubole Cost Explorer allows you to monitor, manage, and optimize big data costs by providing granular visibility of workloads at the job, cluster, and cluster instance levels. With Cost Explorer, you can track spending, monitor show back, justify business plans, and build ROI analyses.

Join us and see how you can:

  • Measure report and optimize your cluster (Spark, Presto, Hive, Airflow, etc.) down to the most granular level.
  • Easily discover the cost of compute on each and every command.
  • Forecast the cost savings to be made by switching from on-demand to spot nodes

Tell us about you so we can tailor your Qubole experience

[preEmptive_forms id=”7″]


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.


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, 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.