Tableau Software [NYSE: DATA] today announced it has acquired HyPer, a high performance database system initially developed as a research project at the Technical University of Munich (TUM). As part of the technology acquisition, Tableau will add key technical personnel and plans to establish a research and development center in Munich and expand its research into high performance computing.
HyPer is a fast main-memory database system designed for simultaneous OLTP and OLAP processing without compromising performance. It also unifies transactions and analysis in a single system, and when coupled with Tableau will help customers take visual analytics closer to the transactional systems that underlie most businesses.
HyPer grew out of a research project started in 2010 by professors Dr. Thomas Neumann and Dr. Alfons Kemper, chair of TUM’s database group. Four of the project’s Ph.D. students, Tobias Muehlbauer, Wolf Roediger, Viktor Leis and Jan Finis, will join Tableau, focused on integrating HyPer into Tableau products.
“HyPer was born at TUM, similarly to how Tableau was founded out of Stanford,” said Muehlbauer. “We have similar approaches to innovation and a shared vision to help the world see and understand data. We’re thrilled that Tableau customers will benefit from our research, and we now have the opportunity to make a big impact in the data analytics space.”
The HyPer team will be based in Munich. Tableau plans to invest additional resources in Munich to leverage the talent from TUM for further innovation that will enhance future Tableau products.
“Munich is a vibrant city with a wealth of talent from TUM,” said Chris Stolte, Chief Development Officer at Tableau. “This technology acquisition is focused on advancing the work HyPer has begun and developing new technologies to advance data analytics as a whole.”
HyPer will be integrated into Tableau’s product lines and bring a host of new capabilities to Tableau customers:
• Faster analysis of data of all sizes
• Enhanced data integration, data transformation and data blending
• Richer analytics, such as k-means clustering and window functions
• Expanded support for Big data efforts with semi-structure and unstructured data
• Unification of analysis and transactional systems