Serverless SQL Engine Targets Cloud Analytics
Qubole Inc., the cloud analytics vendor, has added a serverless engine to its platform aimed at simplifying complex tasks like creating data pipelines and server clusters used to scale analytics workloads in the cloud.
Qubole said this week its Quantum serverless engine enables data analysts to query data up to the petabyte scale via standard SQL. The engine eliminates the need to configure and manage infrastructure. Along with accelerating analytics tasks by reducing IT management overhead, Quantum is promoted as a cost-saver since user only pay for queries run in Amazon Web Services’ object stores or data lakes.
The serverless engine is the latest implementation of the emerging architecture as customers implementing multi-cloud strategies look for tools to improve management of public cloud services. Among the requirements is being able to access data sets across different cloud platforms.
Qubole, Santa Clara, Calif., claims to be the only private company providing a cloud-native data platform with serverless SQL access to big data. Quantum is touted as providing data analysts with a “self-service way to query their big data on the cloud of their choice,” said Ashish Thusoo, CEO and co-founder, Qubole.
“Infrastructure simplification is driving the evolution from on-premises to virtualized datacenters and finally to the serverless environment for both stateless and stateful services,” the company added in a blog post.
Qubole’s data platform runs on Amazon Web Services, Microsoft Azure, Google Cloud and Oracle Cloud using a variety of processing engines, including Apache Spark, Hive and Presto. Quantum allows users to run analytics workloads on either a serverless SQL engine of a managed Presto cluster.
Other features in include query management based on a patent-pending algorithm used to gauge resource allocation. The interface allows users to switch between serverless and managed modes depending on the workload.
The Quantum serverless engine is available now.