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Qubole launches platform to automate big data workloads in the cloud

 

Big data as a service company Qubole Inc. is launching new product today aimed at helping organizations scale out their data initiatives while cutting back on costs.

The products in question are components of Qubole’s Autonomous Data Platform, announced in May, which leverages machine learning technology to automate companies’ big-data workloads. Qubole Data Service Enterprise Edition, Business Edition and Cloud Agents together combine to automate and analyze platform usage to drive greater efficiency, the company said.

Qubole’s autonomous data platform is basically an upgraded version of the original Qubole Data Service that was first released in 2011. QDS was designed to democratize the Apache Hadoop framework by providing a simplified, cloud-based interface through which users can enter simple queries about their data without any particular skills. QDS retrieves data via a series of connectors and uses algorithms to provide answers to user queries. The platform also allocates hardware resources for each specific request, freeing up the infrastructure once it’s finished as a way of minimizing overheads.

Qubole said its new Autonomous Data Platform is necessary because organizations are facing increasing difficulty in analyzing data at scale. The main reason for this is that most organizations are unable to hire the qualified data scientists, engineers and analysts needed to do this. But Qubole reckons it can do away with the need for these human roles simply by automating the manual effort involved.

“Even though big data technologies have greatly advanced, most organizations have trouble operationalizing their big data efforts because data teams simply cannot scale to meet demands for data across the organization,” said Ashish Thusoo, co-founder and chief executive officer of Qubole (pictured). “What’s needed is to remove the manual effort that comes with maintaining a big data infrastructure so that data teams are empowered to focus on high-value, strategic work.”

Qubole claims its Autonomous Data Platform can “self-manage and self-optimize” itself thanks to its machine learning capabilities. The platform does so by analyzing metadata from clusters, queries and users, and also the data it generates. This allows it to provide alerts, insights and recommendations to users, while autonomous agents can also perform many basic functions without the need for human input. For example, the Qubole Spot Shopper Agent can be used to find the best deal at any given time on Amazon Web Services’ Spot Instances, saving companies an average of 68 percent on the cost of using them.

For customers, the great advantage is it allows them to focus on using Qubole’s platform to tackle real business problems, rather than worrying about the technology itself, Qubole said.

The potential of this was not lost on George Gilbert, big data analyst at Wikibon, owned by the same company as SiliconANGLE, who discussed the company in an appearance on theCUBE during the Data Platforms conference in May sponsored by Qubole (below). During the segment, Gilbert said he was impressed with Qubole’s cloud-based big data as a service model, as it allows organizations to extract insights from their data more easily than traditional big data environments.

Gilbert explained that in the cloud, virtual machines are replaced with services. These have the advantage of greatly simplifying administration and development by automating tasks which previously drained human hours and resources, he said.

“So far, the Clouderas and the MapRs and the Hortonworks of the world are more software than service when they’re in the cloud,” Gilbert said. “They don’t hide all the knobs; you still need highly trained admins.”

Qubole’s platform runs on Amazon Web Services, Microsoft Azure and Oracle Bare Metal Cloud, and supports a number of popular big-data technologies besides Hadoop, such as Apache Spark, Hive and Presto.

This article was originally published on siliconangle.com and can be viewed in full

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