SOURCE: Qubole

Qubole

June 16, 2015 08:00 ET

Station X Accelerates Pace of Genome Interpretation With Qubole

Qubole's Big Data-as-a-Service Platform Removes Scaling Hurdles While Driving Faster Discoveries

MOUNTAIN VIEW, CA--(Marketwired - Jun 16, 2015) -  Qubole, the big data-as-a-service company, today announced that Station X, a leading developer of technologies that make large-scale human genome management and analysis easier, is using Presto on Qubole's cloud-based big data platform to power GenePool™, a powerful software-as-a-service solution for real-time analytics of genomic and medical information.

GenePool from Station X allows researchers to manage, visualize and analyze processed genomic data from one to thousands of patients and then securely share the data and resulting knowledge with colleagues. GenePool also provides a convenient and dynamic way to store patient data from clinical trials for updated reporting and for research into the aggregated genomic information.

"Our customers have varying needs: clinical researchers might use GenePool to examine genomic data from a single patient, while a major research institution might use the platform to perform analyses over 10,000 patients at once," said Anish Kejariwal, Senior Director of Engineering at Station X. "Qubole has allowed us to meet the computational needs of our customers working at any scale and ensure that we always have the right amount of compute power for the job -- greatly speeding up the time to insight for GenePool users."

Qubole's self-service platform for big data analytics runs on the three major public clouds: Amazon AWS, Google Compute Engine and Microsoft Azure. It is a fully managed big data offering that leverages the latest open source technologies, such as Presto, which Station X is using, as well as Apache Hadoop, Hive, Pig, Oozie, Sqoop and Spark. The only comprehensive cloud-based data analytics platform, Qubole comes complete with enterprise security features, an easy to use UI and built in data governance.

Station X has integrated Qubole with its GenePool platform to enable real-time cohort-scale genomic analysis using Presto and Hadoop. This integrated solution alleviates the need for GenePool customers to become experts in petabyte scale storage, compute clustering, machine learning and map reduce technologies.

"Station X is enabling their customers to focus on improving how clinicians diagnosis and treat disease -- they shouldn't have to devote valuable capital and brainpower fiddling with technology to carry out their work," said Ashish Thusoo, co-founder and CEO of Qubole. "With the combination of Qubole and GenePool, a research scientists and clinicians can focus on digging into and collaborating around the data to find those hidden patterns that can ultimately lead to life-saving discoveries. That's the real power of big data in the cloud."

About Station X
Station X, Inc. was founded in 2010 by leaders in the fields of genomics and computational biology. The company's flagship product, GenePool, enables scientists and clinicians working with large-scale human genome information to manage, organize, mine and share their data sets in a scalable, secure environment. Station X is based in San Francisco and can be found online at www.stationxinc.com.

About Qubole
Qubole is a big data-as-a-service company that provides a fast, easy and reliable path to turn big data into valuable business insights. Qubole's cloud-based platform addresses the challenges of processing huge volumes of structured and unstructured data. It uses clouds such as Amazon Web Services, Google Compute and Microsoft Azure to help enterprises put big data processing in the hands of their users while enabling their operations teams to be nimble and adaptive to their users' needs. Qubole achieves this through features such as auto-scaled big data clusters and integrated tool sets for data analysts, developers and business users. With more than 100 PB of data processed every month across its customer base, Qubole's platform makes enterprises agile with big data.

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