Not all of your company’s data is accessed frequently, which results in the potential for improvement where performance and costs (TCO) are concerned. In this session, we present a partitioning concept for actual and historical enterprise data that sets the groundwork for efficient OLTP scale-up and OLAP scale-out, thus allowing for further scaling of the performance of columnar in-memory databases while lowering TCO.
The speaker, Stefan Klauck, is a research assistant at the chair of Prof. Hasso Plattner at the Hasso Plattner Institute, Germany. In the research area “In-Memory Data Management for Enterprise Systems“, Stefan and his colleagues conduct research projects with the goal of improving main memory databases for both transactional and analytical queries in a single system. Enterprise databases for mixed workloads enable interactive analytics on transactional data.