Qubole delivers faster access to petabytes of secure, reliable and trusted datasets of structured and unstructured data for Analytics and Machine Learning. Users conduct ETL, analytics, and AI/ML workloads efficiently in end-to-end fashion across best-of-breed open source engines, multiple formats, libraries, and languages adapted to data volume, variety, SLAs and organizational policies.
Qubole provides an out-of-the-box workbench and notebooks for data scientists, data engineers, data analysts, and administrators. It supports open source frameworks used by every type of data user including Apache Spark, Presto, Hive/Hadoop, TensorFlow, and Airflow.
Qubole automates the installation, configuration, and maintenance of clusters, multiple open source engines, and purpose-built tools for data exploration, ad-hoc analytics, streaming analytics and machine learning. Organizations realize administrator-to-user ratios of 1:200 or higher and near-zero administration experience.
Qubole’s Workload-aware autoscaling and real-time spot buying drives down compute costs dramatically. Pre-configured financial governance policies and built-in optimization lower data lake cloud computing costs continuously while providing administrator overrides to accommodate special needs.
Watch Qubole co-founder Ashish Thusoo discuss the analytics and machine learning use cases that are driving the demand for open data lakes
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