Main memory databases for enterprise applications

Enterprise applications are traditionally divided in transactional and analytical processing. This separation was essential as growing data volume and more complex requests were no longer performing feasibly on conventional relational databases. While many research activities in recent years focussed on the optimization of such separation particularly in the last decade databases as well as hardware continued to develop. On the one hand there are data management systems that organize data column-oriented and thereby ideally fulfill the requirement profile of analytical requests. On the other hand significantly more main memory is available to applications that allow to store the complete compressed database of an enterprise in combination with the equally significantly enhanced performance. Both developments enable processing of complex analytical requests in a fraction of a second and thus facilitate complete new business processes and -applications. Obviously the question arises whether the artificially introduced separation between OLTP and OLAP can be revoked and all requests be handled on a combined data set. This paper focuses on the characteristics of data processing in enterprise applications and demonstrates how selected technologies can optimize data processing. A further trend is the use of cloud computing and with it the outsourcing of the data centre to optimize cost efficiency. Here column-oriented in memory databases are helpful as well as they permit a greater throughput, which in turn enables more effective use of the hardware and thus saves costs.