Tableau Acquires HyPer; Terms Not Dislosed
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
"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
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