Using AWS shouldn’t mean overpaying for big data processing.

Qubole customers on AWS save an average of 50% on big data costs and have one admin supporting 200+ users. Learn how to optimize your big data cloud costs with Qubole.

ACHIEVE 3x FASTER
TIME TO VALUE

Deploy new use cases in days. Don’t waste time splitting workloads into multiple clusters.

IMPROVE RELIABILITY WITH WORKLOAD-AWARE AUTOSCALING

Autoscale and rebalance clusters based on workload need to prevent cost overruns.

INCREASE RESILIENCY OF SPOT INSTANCES

Mitigate the risk of Spot losses with rebalancing, intelligent planning, and rapid job recovery.

MAXIMIZE RESOURCES WITH SELF-OPTIMIZING PLATFORM

Choose the best cluster configuration for the job. Automatically shut down idle clusters.

Why Qubole Is Better

Reduce CAPEX and OPEX by 50% or More

Significantly reduce cloud computing costs with sustainable economics through built-in capabilities such as workload-aware autoscaling, aggressive cluster downscaling, and heterogeneous cluster configurations.

Use Spot Instances Without Fear of Job Loss

Automate and optimize usage of Spot instances while maintaining reliability with Spot rebalancing, proactive autoscaling, fault tolerance, and risk mitigation. Among Qubole customers, Spot instances account for 54% of all AWS compute hours — with estimated savings of $230 million.

Execute Any ML, AI, or Analytics Use Case

Qubole includes an out-of-the-box workbench and notebooks for data scientists, data engineers, data analysts, and administrators. Our platform serves a wide landscape of use cases, and we support open source frameworks used by every type of data user including Apache Spark, Presto, Hive/Hadoop, TensorFlow, and Airflow.

Achieve a 1:200 Admin-to-User Ratio

Increase the number of users per administrator without impacting your budget. Qubole’s self-service platform allows one administrator to support 200+ users, so you can do more with less.

Get Dedicated Support Every Step of the Way

Unlike the self-service approach other vendors take, the Qubole team provides unmatched support for users through training, technical support, on-demand courses, best practice recommendations, and in-person sessions.

See What Our Customers Are Saying

VIDEO TESTIMONIAL
Return Path
VIDEO TESTIMONIAL
MediaMath
The overwhelming reason we selected Qubole was because it is the only vendor that offers what I consider to be true autoscaling. By true autoscaling, I mean that autoscaling is self-service — if the load on our cluster is high, the cluster automatically expands. Conversely, nodes are automatically removed when the load is low. This is different from manual autoscaling where you need to pre-define autoscaling capacity. Shailesh Garg, CTO and Head of Analytics at Komli Media
We've been using Qubole for over a year and the best part about it is it's actually a managed service. It's a pretty innovative big data service that aggressively takes advantage of Spot pricing offered by AWS. I don't know of any other tool that takes advantage of Spot pricing as aggressively as Qubole does. Yekesa Kosuru, VP of Technology, DataXu
We're down one-third of what we were originally paying for spot, so operations is very happy because, one, they don't have to retune their jobs. Craig Carl, Dir., Solutions Architecture, Bare Metal Cloud Team, Oracle

Learn More

PLATFORM OVERVIEW
Adaptive Serverless Platform
BLOG
Up to 80% savings with AWS Spot Instances
CASE STUDY
Oracle Uses Heterogeneous Clusters to Increase Cost Efficiency
WEBINAR
Introduction to Qubole: A Data Platform Built to Scale

Join our Weekly Product Demo to see how Qubole supports any ML, AI or Analytics use case