Deploy new use cases in days. Don’t waste time splitting workloads into multiple clusters.
Autoscale and rebalance clusters based on workload need to prevent cost overruns.
Mitigate the risk of Spot losses with rebalancing, intelligent planning, and rapid job recovery.
Choose the best cluster configuration for the job. Automatically shut down idle clusters.
Significantly reduce cloud computing costs with sustainable economics through built-in capabilities such as workload-aware autoscaling, aggressive cluster downscaling, and heterogeneous cluster configurations.
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.
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.
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.
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.
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.