Qubole Data Service (QDS) supports Microsoft Azure Data Lake Store, Azure optimized workload aware auto-scaling



Qubole, a big data-as-a-service company, announced Wednesday integration with Microsoft Azure. Now, Qubole Data Service (QDS) supports Microsoft Azure Data Lake Store and adds workload aware auto-scaling capabilities that leverage Azure’s minute-based billing model for an up to 66 percent reduction in total cost of ownership (TCO).

The Qubole platform leverages a range of big data intelligent solutions, including artificial intelligence (AI) and machine learning (ML) technologies to deliver a more accessible way to perform big data analytics on data stored in Azure. QDS’ native support for Microsoft Azure Data Lake Store provides a seamless way to perform analytics on Microsoft’s high-performance, scalable data store and is fully compliant with the Azure security framework.

Big data workloads tend to be “bursty.” The amount of compute required can vary greatly over time. Azure cloud supports per-minute billing and when combined with QDS’ unique workload aware auto-scaling, creates a precise way to reduce wasted compute, lowering TCO by as much as 66 percent.

Natively designed for Microsoft Azure and tightly integrated with its storage, compute, security and other key architectural elements, QDS on Azure is the best Autonomous Data Platform for any organization implementing big-data projects on Microsoft Azure.

The Qubole Application Tier orchestrates resources on the customer’s Azure account using customer-provided and revocable Azure Identity security roles. Meta-data describing the data on Azure Blob is stored in the Hive Metastore in the Qubole tier or, if required, on the customer’s account.

Workload requests are submitted by users via a web-based Workbench or via Spark Notebooks, by external applications via REST APIs, and by third party Business Intelligence products via ODBC/JDBC drivers. Query results are cached locally by Qubole for future re-use to minimize use of compute resources.

“Acxiom connects companies with their customers through data and analytics to provide a seamless experience. To maintain our leadership and drive innovation in this industry, our research team leverages machine learning and big data technologies running on Azure. We are able to maximize our cloud computing spend by using Qubole’s leading big data platform and its deep integration with Microsoft Azure,” said Joe Hsy, Director of Engineering, Acxiom Research. “Qubole’s experience optimizing big data workloads enables us to conduct critical research and innovation at significantly lower costs.”

“Enterprise customers are increasingly turning to Microsoft Azure as their public cloud infrastructure provider and the Azure Data Lake Store provides the scale and performance they require for big data workloads. Qubole’s deep integration with Azure provides enterprises with an ideal solution for processing that data and turning it into valuable business insights,” said Ashish Thusoo, co-founder and CEO of Qubole. “We are committed to helping enterprises leveraging Azure to transform their businesses with big data insights.”

“Qubole’s support compliments Microsoft Azure Data Lake Store by providing a turnkey analytics processing layer that makes it easy for customer to start solving business problems at scale, very quickly,” said Bharat Sandhu, director, product marketing, Microsoft AI. “Qubole offers the tools and services that enable customers to optimize their big data workloads along with the automation necessary to keep costs down and prevent the need to put heavy demands on internal resources.”

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