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June 07, 2018
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Hortonworks at ACM SIGMOD/PODS 2018

The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. This year ACM SIGMOD/PODS will be held in Houston, TX, USA on June 10th – June 15th, 2018, and Hortonworks will be present in the conference, contributing to and learning from the broader research community.

First, we will be presenting a paper that formally introduces Apache Calcite to the research community, briefly reviews its history, and describes its architecture, features, functionality, and patterns for adoption. Apache Calcite has become a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems, and Hortonworks has helped to nurture and grow the Calcite project and its community over the years. In particular, Calcite was one of the centerpieces of the Stinger initiative whose main goal was to improve Apache Hive in three different axes: latency, scalability, and SQL support. Since then, each release of Hive has tighten its integration with Calcite, including new features for its optimizer aimed to generate better plans and deliver improvements to query execution.

In addition, Apache Hive and Apache Pig will be recognized with the SIGMOD Software Systems Award “for developing seminal software systems that served to bring relational-style declarative programming to the Hadoop ecosystem” during the conference. This is a tremendous achievement for the two Apache communities that have helped to develop these projects and for everyone who has contributed to them.

If you are attending the conference, we hope you will get in touch with us to discuss our work and also opportunities to contribute to these and other projects in this thrilling open-source ecosystem!

Paper: Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Edmon Begoli, Jesús Camacho Rodríguez, Julian Hyde, Michael J. Mior, and Daniel Lemire


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