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Qubole Open Sources Quark for SQL Virtualization

  • By Ari Amster
  • April 5, 2016
 

Qubole, the big data-as-a-service company, today announced that it has open sourced Quark, a cost-based SQL optimizer that helps to simplify and optimize access to data for data analysts. Traditionally, the data sets generated by data teams are aggregated and copied to multiple analytics systems to balance performance and cost, making it near impossible to query the data in a way that can quickly inform business decisions. Quark simplifies and optimizes access to data by managing relationships between tables across an organization’s databases.

The Quark project is available in open-source, as well as in a SaaS implementation via the Qubole Data Service (QDS). Quark models relationships between datasets using well-known database concepts like materialized views and OLAP cubes, enabling data analysts to automatically take advantage of the fastest and most efficient data set for their queries.

“We at Qubole are committed to the open source community. With the open sourcing of Quark, we are offering a simple, optimized and compatible solution to allow developers to route SQL queries across data warehouses, big data SQL engines, and data marts. Open source is core to our values at Qubole, and that’s why we built our QDS platform to be agnostic and easily integrate with most open source data engines,” said Ashish Thusoo, co-founder, and CEO of Qubole.

Qubole’s co-founders, Ashish Thusoo and Joydeep Sen Sarma, are committers to open source projects and have authored several prominent open source projects, including the Apache Hive Project.

Quark is distributed as a JDBC jar and works with the majority of tools that integrate with JBDC. Qubole’s customers can take advantage of Quark-as-a-service directly. Qubole’s global shared Hive metastore provides the central view into data along with connectivity to data warehouse engines such as Redshift and relational databases. Quark on Qubole automatically optimizes across data engines and data sets. For more information, please contact: [email protected]


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