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Qubole Expands Machine Learning and Analytics Capabilities On Microsoft Azure
September 20, 2018 News analytics Machine Learning

Qubole, the data activation company, announced the Qubole Data Platform now supports Power BI, the market-leading business analytics service from Microsoft. The new integration with Power BI, powered by Qubole’s Presto implementation, provides customers with the only offering on Azure that can federate Power BI queries across diverse big data sources—enabling users to interact with, analyze, and uncover new insights.

Qubole is natively designed for Microsoft Azure, offering tight integration across storage, compute, security, and other key elements of the Azure cloud architecture. The integration with Power BI takes that to another level, introducing a suite of new interactive analytics and data visualization features for Qubole customers on Azure. Users can connect to hundreds of data sources, simplify data preparation, and drive ad-hoc analysis to deliver insights to teams throughout their organization.

Qubole Presto and Power BI enable businesses to run self-service federated queries from unstructured, semi-structured, and structured data sources for reports, visualizations, mashups, and powerful dashboards published in the Power BI Service. Qubole Presto is the only Presto distribution on the market that delivers advanced optimizations, caching, auditing, a query cost model, and workload-aware autoscaling, offering the best cloud-native performance and lowest operational costs. Moreover, by introducing Presto for querying and processing data, users are no longer constrained by a single big data source or by the latency of local processing of queries against vast amounts of big data, dramatically improving workload efficiency.

Furthering the new integration, Qubole has also been chosen to join a select group of partners in the new Azure Data Lake Store (ADLS) Gen 2 preview program, launched in June 2018. Organizations can now process exabytes of big data with the leading cloud-native big data platform for artificial intelligence (AI), machine learning (ML) and analytics from Qubole against ADLS Gen 2, which delivers the security, massive scalability, and cost benefits of object storage, and the reliability and performance typically associated only with on-premises file systems.

Qubole on Azure has become core to the big data strategy for a range of companies, such as Malaysia Airlines. By incorporating Qubole as a key layer of their new infrastructure, Malaysia Airlines dramatically increased data processing and analytics capabilities, while reducing data ingestion time by more than 90 percent. In fact, with Qubole on Microsoft Azure, Malaysia Airlines is now able to process pricing and customer data in near real-time, and all of the data is easily stored, managed and analyzed in the cloud.

For Finning International—the largest global distributor of Caterpillar trucks, equipment and services—the business impact is clear: “The speed and scalability of Qubole on Azure enables data scientists at Finning to focus on delivering value from data on our customers’ connected equipment, without having to manage the IoT infrastructure of the platform,” says Sarb Sarkaria, Senior Data Architect at Finning. “That’s where the benefit of this solution really makes a difference for us.”

As the industry’s first cloud-native data platform, Qubole self-manages, self-optimizes, and learns from data usage, allowing data teams to help drive business outcomes rather than focus on managing a platform. Qubole’s intelligent automation and self-service access supercharge productivity, while workload-aware autoscaling drives down compute costs to ensure sustainable cloud economics.

“As the shift to cloud accelerates, businesses are looking to remove complexity and costs while knowing their data platforms can grow and evolve with them as technologies advance,” said Ashish Thusoo, co-founder and CEO, Qubole. “By introducing support for Power BI with Presto, Qubole is providing Azure users with the tools to simplify and automate big data and ML operations, drive down costs, and optimize results.”